Cited by 815Speech and dialogue systemsAI in Service InteractionsSocial Robot Interaction and HRI

The DOI matches its Crossref record; 73 of the 73 references it cites were found in a registry or an index; none of the 27 sources its claims cite is checked for support yet.

The Design and Implementation of XiaoIce, an Empathetic Social Chatbot

Open access

Zhou, Li, Gao, Jianfeng, Li, Di, Shum, Heung-Yeung·2020·Computational Linguistics

DOI 10.1162/coli_a_00368

Open this paper in CoChat — free account

Abstract

This article describes the development of Microsoft XiaoIce, the most popular social chatbot in the world. XiaoIce is uniquely designed as an artifical intelligence companion with an emotional connection to satisfy the human need for communication, affection, and social belonging. We take into account both intelligent quotient and emotional quotient in system design, cast human–machine social chat as decision-making over Markov Decision Processes, and optimize XiaoIce for long-term user engagement, measured in expected Conversation-turns Per Session (CPS). We detail the system architecture and key components, including dialogue manager, core chat, skills, and an empathetic computing module. We show how XiaoIce dynamically recognizes human feelings and states, understands user intent, and responds to user needs throughout long conversations. Since the release in 2014, XiaoIce has communicated with over 660 million active users and succeeded in establishing long-term relationships with many of them. Analysis of large-scale online logs shows that XiaoIce has achieved an average CPS of 23, which is significantly higher than that of other chatbots and even human conversations.

AI snapshot

Written from the paper’s full text.

TL;DR

This paper describes Microsoft XiaoIce, an empathetic social chatbot designed as an AI companion with IQ, EQ, and personality, optimized for long-term user engagement measured by expected Conversation-turns Per Session (CPS). It reports system architecture, dialogue manager, empathetic computing, Core Chat, skills, and large-scale deployment results, including 660 million active users and an average CPS of 23.

Key findings

XiaoIce was launched in May 2014 and by July 2018 had over 660 million active users, was deployed in five countries on more than 40 platforms, and achieved an average CPS of 23 in its 6th generation; the paper compares this with CPS 9 for human conversations in its user study and CPS 14.6 for the latest Amazon Alexa systems per Khatri et al. [2018].
The empathetic computing module’s A/B test on Weibo showed no significant CPS change but increased NAU from 0.5 million to 5.1 million in 3 months; it was released in July 2018 and became the most important feature in the 6th generation.
Core Chat’s hybrid approach improved expected CPS by 0.5 in two weeks over a retrieval-only baseline; incorporating the neural generator and unpaired-data retrieval improved response coverage by 20% and 10%, respectively. Topic Manager improved Core Chat expected CPS by 0.5, and Image Commenting doubled expected CPS across all dialogues containing images.
The persona-based neural response generator achieved perplexity 25.0 (−8.4% vs. seq2seq) and BLEU 1.9% (+18.8% vs. seq2seq, +11.8% vs. LSTM-MMI) on the TV series dataset; the XiaoIce Image Commenting prototype scored BLEU-4 4.53, ROUGE-L 15.33, CIDEr-D 3.21, METEOR 15.51, and SPICE 4.82, outperforming four state-of-the-art image captioning systems.

Methods

Design: XiaoIce is a modular system with user experience, conversation engine, and data layers; social chat is cast as hierarchical decision-making over options in Markov Decision Processes, with a dialogue manager, empathetic computing module, Core Chat, and 230 dialogue skills, optimized for expected CPS.
Data and training: paired conversational data included more than 30 billion conversation pairs as of May 2018, with 70% of XiaoIce’s responses retrieved from her own past conversations; classifiers for user understanding were trained on 10K manually labeled dialogue sessions, the response ranker on 50K manually labeled dialogues, neural generation and ranking features on 50 million human dialogues, and Image Commenting on 28 million images each paired with 6 text comments.
Evaluation: A/B tests used expected CPS and NAU; pilot studies used human ratings on a 3-level quality scale and benchmark metrics such as perplexity, BLEU, METEOR, CIDEr, ROUGE-L, and SPICE; large-scale online logs were analyzed over long periods.

Limitations

The paper states that automatic evaluation of open-domain dialogue systems is “by no means a solved problem,” that common automatic metrics have limitations, and that a unified modeling framework for XiaoIce “remains to be proved” effective.
The paper states that fully grounding all conversations in the physical world for goal-oriented interactions remains a non-trivial challenge, and that the fundamental mechanism of human-level intelligence is not yet fully understood.
The paper discusses ethical concerns including privacy, who is in control, unrealistic user expectations, and potential misuse; evident from the text, many key results come from proprietary A/B tests and online logs without reported confidence intervals or full statistical details, and the comparison to human conversations relies on an unspecified user study.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1162/coli_a_00368

Cites

73 of 73 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

0 of 27 cited sources checked · 0 supported · 0 flagged

The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Although the overall quality of the candidates generated from the unpaired database is lower than those retrieved from the paired database, with unpaired database XiaoIce can cover a much broader range of topics.”
Load-bearingStrength:plain· not yet calibratedCausal:direct· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
- Rests on an assumption: The unpaired database sentences from public lectures and quotes are suitable as candidate responses for XiaoIce.

Show all 73 claims

Probably not a substantial claim. 66
It may state two separate claims as one. 43
The quote only partly states this claim. 2

The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Since the release in 2014, XiaoIce has communicated with over 660 million active users and succeeded in establishing long-term relationships with many of them.”
- Also stated in: § 1 Introduction · p. 1 · plain
  “Since her launch in China in May 2014, XiaoIce has attracted over 660 million active users (i.e., subscribed users).”
Strength:plain· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: fails
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“As shown in Table 1, the persona model significantly outperforms both baselines, achieving a lower perplexity (-8.4%) and a higher BLEU score (+18.8% and +11.8%) Papineni et al. [2002].”
What the sources say
- Not checked yetcites Papineni et al. [2002]
Load-bearingStrength:boosted· not yet calibratedCausal:direct· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
It may state two separate claims as one.
In the paper
“On May 15, 2018, XiaoIce published the first AI-created Chinese poem album in history 11 .”
What the sources say
- Not checked yetcites [11] Cuayáhuitl Heriberto, 2018
Strength:plain· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passes
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The dialogue policy is designed to optimize long-term user engagement through an iterative, trialand-error process based on the feedback of XiaoIce's users.”
Strength:plain· not yet calibratedCausal:direct· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: fails
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
- The authors state this as an assumption: Human-machine social chat can be modeled as a hierarchical decision-making process over Markov Decision Processes.
- The authors state this as an assumption: Expected CPS is a valid measure of long-term user engagement for social chatbots.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This paper describes the development of Microsoft XiaoIce , the most popular social chatbot in the world.”
- Also stated in: § 1 Introduction · p. 1 · plain
  “In this paper we present the design and implementation of Microsoft XiaoIce ('Little Ice' literally in Chinese), the most popular social chatbot in the world.”
Strength:plain· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passes
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“More importantly, social chatbots also require a sufficient EQ to meet users' emotional needs, such as emotional affection and social belonging, which are among the fundamental needs for human beings Maslow [1943].”
What the sources say
- Not checked yetcites Maslow [1943]
Load-bearingStrength:plain· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Thus, XiaoIce has to interact with the user's environment and access real-world knowledge e.g., via API calls.”
Load-bearingStrength:plain· not yet calibratedCausal:direct· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
It may state two separate claims as one.
- Rests on an assumption: Traditionally, social chatbots are designed for chitchat scenarios where bots are expected to mimic human user conversations but not to interact with the user's environment.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Each generated response is labeled on the 3-level quality scale by three human judges.”
Load-bearingStrength:plain· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
Probably not a substantial claim.
- Rests on an assumption: The 3-level quality scale labels assigned by human judges reliably measure response quality.
In the paper
“According to Khatri et al. [2018], the latest Alexa systems have achieved the CPS of 14.6, an increase of 54% since the launch of the 2018 competition.”
What the sources say
- Not checked yetcites Khatri et al. [2018]
Strength:plain· not yet calibratedCausal:direct· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passes
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“The second is the LSTM-MMI model Li et al. [2016a], which is one of the state-of-the-art neural response generation models.”
What the sources say
- Not checked yetcites Li et al. [2016a]
Strength:plain· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passes
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Therefore, low BLEU (or other metrics) scores do not necessarily indicate low quality as the number of reference responses is always limited in the test set.”
Strength:plain· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes
It may state two separate claims as one. The quote only partly states this claim.
- Rests on an assumption: In open-domain dialog systems, the same input may have many plausible responses that differ in topics or contents significantly.
- Rests on an assumption: The number of reference responses in the test set is always limited.
In the paper
“Galley et al. [2015] showed that the correlation of string-based metrics (e.g., BLEU and deltaBLEU) significantly increases with the units of measurement bigger than a sentence.”
What the sources say
- Not checked yetcites Galley et al. [2015]
Strength:boosted· not yet calibratedCausal:direct· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passes
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“But as pointed out in Gao et al. [2019], the correlation analysis by Liu et al. [2016] is performed at the sentence level while BLEU is designed from the outset to be used as a corpus-level metric.”
What the sources say
- Not checked yetcites Gao et al. [2019]
- Not checked yetcites Liu et al. [2016]
Load-bearingStrength:plain· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
It may state two separate claims as one.
In the paper
“Retrieval-based methods are often evaluated using traditional information retrieval metrics Manning et al. [2008] such as as Precision@K, Mean Average Precision (MAP), and normalized Discounted Cumulative Gain (nDCG).”
What the sources say
- Not checked yetcites Manning et al. [2008]
Strength:plain· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passes
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It is not surprising, then, that with vastly more people being digitally connected in the social media age, social chatbots have become an important alternative means for engagement.”
Strength:plain· not yet calibratedCausal:direct· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: fails
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
- Rests on an assumption: Happiness and meaningful conversations often go hand in hand.
None of the sources cited for this claim have been checked yet.
In the paper
“Generation-based methods are evaluated using those metrics borrowed from text generation tasks like machine translation and text summarization, using string and n-gram matching metrics such as BLEU Papineni et al. [2002], METEOR Banerjee and Lavie [2005] and ROUGE Lin [2004].”
What the sources say
- Not checked yetcites Papineni et al. [2002]
- Not checked yetcites Banerjee and Lavie [2005]
- Not checked yetcites Lin [2004]
Strength:plain· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passes
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“However, as argued by Gao et al. [2019], machine-learned metrics lead to potential problems such as overfitting and 'gaming of the metric' Albrecht and Hwa [2007].”
What the sources say
- Not checked yetcites Gao et al. [2019]
- Not checked yetcites Albrecht and Hwa [2007]
Strength:hedged· not yet calibratedCausal:direct· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passes
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“deltaBLEU Galley et al. [2015] is an extension to BLEU that exploits numerical ratings associated with conversational responses.”
What the sources say
- Not checked yetcites Galley et al. [2015]
Strength:plain· not yet calibratedCausal:correlational· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passes
Probably not a substantial claim. The paper’s conclusions may not depend on it.

89 of 89

The authors state this as an assumption.
Human-machine social chat can be modeled as a hierarchical decision-making process over Markov Decision Processes.
“we mathematically cast human-machine social chat as a hierarchical decision-making process, and optimize XiaoIce for long-term user engagement, measured in expected CPS.”
“cast human-machine social chat as decision-making over Markov Decision Processes (MDPs)”
The authors state this as an assumption.
Expected CPS is a valid measure of long-term user engagement for social chatbots.
“we propose to use expected Conversation-turns Per Session (CPS) as the success metric for social chatbots.”
“The larger the CPS is, the better engaged the social chatbot is.”
The paper relies on this without support.
An AI companion can satisfy the human need for communication, affection, and social belonging.
“XiaoIce is uniquely designed as an AI companion with an emotional connection to satisfy the human need for communication, affection, and social belonging.”
The paper relies on this without support.
The machine learning classifiers used for user understanding accurately detect user intents, emotions, topics, opinions, and persona from dialogue.
“These key-value pairs are generated using a set of machine learning classifiers as follows.”
The paper relies on this without support.
The TV series dataset used in the first pilot study is representative enough to validate the persona-based neural response generator for XiaoIce.
“In the first pilot study reported in Li et al. [2016b], we compare the persona model against two baseline models, using the TV series dataset for model training and evaluation.”
The paper relies on this without support.
The 3-level quality scale labels assigned by human judges reliably measure response quality.
“Each generated response is labeled on the 3-level quality scale by three human judges.”
The paper relies on this without support.
The KG triples that co-occur often in the same conversations represent related topics humans often discuss.
“We include in the XiaoIce KG a Satori triple ( h, r, t ) only if h and t co-occur often enough in the same conversations. Such a triple contains a pair of related topics ( h, t ) that humans often discuss in one conversation”
The paper relies on this without support.
The unpaired database sentences from public lectures and quotes are suitable as candidate responses for XiaoIce.
“The unpaired database we have used in XiaoIce consists of sentences collected from public lectures and quotes in news articles and reports. These sentences are considered candidate responses R .”
The paper relies on this without support.
Any individual dialogue skill is unlikely to have a significant impact on CPS.
“Since any individual skill is unlikely to have a significant impact on CPS, we measure the user satisfaction of a skill by monitoring its active users and skill triggering rate (i.e., the number of times a skill is activated by users within a day or a week).”
The paper relies on this without support.
Traditionally, social chatbots are designed for chitchat scenarios where bots are expected to mimic human user conversations but not to interact with the user's environment.
“Traditionally, social chatbots are designed for chitchat scenarios where the bots are expected to mimic human user conversations but not to interact with the user's environment.”
The paper relies on this without support.
Happiness and meaningful conversations often go hand in hand.
“Psychological studies show that happiness and meaningful conversations often go hand in hand.”
The paper relies on this without support.
The long-term success of a social chatbot needs to be measured by its user engagement.
“the long-term success of a social chatbot needs to be measured by its user engagement”
The paper relies on this without support.
The most reliable evaluation is to deploy the chatbot to users and monitor user feedback and engagement over a long period.
“There is no doubt that the most reliable evaluation is to deploy the chatbot to users and monitor the user feedback and engagement, measured by user ratings, NAU, CPS, and so on, over a long period of time.”
The paper relies on this without support.
In open-domain dialog systems, the same input may have many plausible responses that differ in topics or contents significantly.
“Nevertheless, in open-domain dialog systems, the same input may have many plausible responses that differ in topics or contents significantly.”
The paper relies on this without support.
The number of reference responses in the test set is always limited.
“as the number of reference responses is always limited in the test set”

15 of 15

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

Pilot-study dataset is reported as both 4K and 5K sessions
Minor
The description of the second pilot study says its evaluation data consists of 4K dialogue sessions, but the caption of the resulting Table 3 calls it a 5K dialogue dataset.
Image Commenting generator direction is reversed
Minor
The Image Commenting section describes a generator that produces text from images, but its evaluation calls the trained component a text-to-image generator.

2 of 2

A theory of human motivation.
Maslow, A. H. (1943) · Psychological Review · doi:10.1037/h0054346 · Cited by 21,563
BLEU: a method for automatic evaluation of machine translation
Papineni, K., Roukos, S., Ward, T., & Zhu, W.-J. (2001) · Proceedings of the 40th Annual Meeting on Association for Computational Linguistics - ACL '02 · doi:10.3115/1073083.1073135 · Cited by 21,344
Sequence to Sequence Learning with Neural Networks
Sutskever, I., Vinyals, O., & Le, Q. V. (2014) · arXiv · doi:10.48550/arxiv.1409.3215 · Cited by 17,766
Introduction to information retrieval
(2009) · Choice Reviews Online · doi:10.5860/choice.46-2715 · Cited by 12,278
A THEORY OF HUMAN MOTIVATION
Axford, N. (2017) · Routledge eBooks · doi:10.4324/9781315258324-16 · Cited by 11,329
Introduction to Information Retrieval
Manning, C. D., Raghavan, P., & Schütze, H. (2008) · doi:10.1017/cbo9780511809071 · Cited by 11,288
Untitled work
(2004) · Cited by 9,718
Affective Computing
Picard, R. W. (1997) · MIT Press · doi:10.7551/mitpress/1140.001.0001 · Cited by 7,382
On the Properties of Neural Machine Translation: Encoder–Decoder Approaches
Cho, K., van Merriënboer, B., Bahdanau, D., & Bengio, Y. (2014) · SSST@EMNLP · doi:10.3115/v1/w14-4012 · Cited by 7,251
Show and tell: A neural image caption generator
Vinyals, O., Toshev, A., Bengio, S., & Erhan, D. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298935 · Cited by 6,318

Flagged first, then by citations

Show all 66 references

Research works printed but not linked: 23

Albrecht Joshua, 2007, Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics, 880
Asimov I., 1984, “The Bicentennial Man” in I. Asimov, The Bicentennial Man and Other Stories
Banerjee Satanjeev, 2005, Proceedings of the ACL Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization, 65
Brahnam Sheryl, 2005, Abuse: The Darker Side of Human Computer Interaction, 62
Cuayáhuitl Heriberto, 2018, NeurIPS Workshop on Conversational AI
Dinan Emily, 2018, CoRR
Fang Hao, 2017, Alexa Prize Proceedings
Fung Pascale, 2016, CoRR
Gan Chuang, 2017, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 3137
Ghazvininejad Marjan, 2018, Proceedings of AAAI, 5111
Lin Chin-Yew, 2004, Proceedings of the ACL workshop, 74
Mathews Alexander Patrick, 2016, AAAI, 3574
Misu Teruhisa, 2012, Proceedings of the 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue, 84
Mostafazadeh Nasrin, 2017, Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers), 462
Papineni Kishore, 2002, Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, 311
Sai Ananya, 2019, Proceedings of AAAI 2019, 33, 7281
Serban Iulian Vlad, 2016, AAAI, 16, 3776
Serban Iulian Vlad, 2017, AAAI, 3295
Shum Heung-Yeung, 2018, CoRR
Sutskever Ilya, 2014, NIPS, 3104
Vinyals Oriol, 2015, ICML Deep Learning Workshop
Wu Bowen, 2016, Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, 652
Xing Chen, 2017, AAAI, 17, 3351

Sorted by citations

Items 201–300 of 815

Empathy-driven Arabic Conversational Chatbot
2020, T. 2. I. C. O. C. L., Hajj, H. M., Hokayem, C., & Naous, T. (2020) · Underline Science Inc. · doi:10.48448/sjrf-2w66 · Cited by 24
The search for AI value: The role of complexity in human-AI engagement in the financial industry
Payne, E. H. M., & O'Brien, C. A. (2024) · Computers in Human Behavior Artificial Humans · doi:10.1016/j.chbah.2024.100050 · Cited by 23
Neural-Symbolic Reasoning over Knowledge Graphs: A Survey from a Query Perspective
Liu, L., Wang, Z., & Tong, H. (2025) · ACM SIGKDD Explorations Newsletter · doi:10.1145/3748239.3748249 · Cited by 31
Target-guided Emotion-aware Chat Machine
Wei, W., Liu, J., Mao, X.-L., Guo, G., Zhu, F., Zhou, P., Hu, Y., & Feng, S. (2021) · ACM Transactions on Information Systems · doi:10.1145/3456414 · Cited by 23
Ontology-based Integration of Knowledge Base for Building an Intelligent Searching Chatbot
Nguyen, H. D., Tran, T.-V., Pham, X.-T., Huynh, A. T., & Do, N. V. (2021) · Sensors and Materials · doi:10.18494/sam.2021.3264 · Cited by 23
Accompanying Reflection Processes by an AI-Based StudiCoachBot: A Study on Rapport Building in Human-Machine Coaching Using Self Disclosure
Mai, V., Wolff, A., Richert, A., & Preusser, I. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-90328-2_29 · Cited by 24
Emotion-Aware Transformer Encoder for Empathetic Dialogue Generation
Goel, R., Susan, S., Vashisht, S., & Dhanda, A. (2021) · International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) · doi:10.1109/aciiw52867.2021.9666315 · Cited by 27
Deep Broad Learning for Emotion Classification in Textual Conversations
Peng, S., Zeng, R., Liu, H., Cao, L., Wang, G., & Xie, J. (2023) · Tsinghua Science & Technology · doi:10.26599/tst.2023.9010021 · Cited by 23
The effect of Arabic language type on banking chatbots adoption
Hmoud, H., Shishan, F., Qasem, Z., & Bazi, S. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e20686 · Cited by 23
Proceedings of the 2nd Workshop on Natural Language Processing for Conversational AI
(2020) · doi:10.18653/v1/2020.nlp4convai-1 · Cited by 23
Tourist interaction and satisfaction with the chatbot evokes pre-visit destination image formation? A case study
Orden-Mejía, M. A., & Huertas, A. (2022) · Anatolia · doi:10.1080/13032917.2022.2072918 · Cited by 22
Socially Interactive Agents as Peers
Cassell, J. (2022) · ACM eBooks · doi:10.1145/3563659.3563670 · Cited by 23
Chatbots in the Field of Mental Health: Challenges and Opportunities
Viduani, A. C., Cosenza, V., Araújo, R., & Kieling, C. (2023) · Springer eBooks · doi:10.1007/978-3-031-10698-9_8 · Cited by 25
Exploring Tertiary Vietnamese EFL Students’ Engagement in Vocabulary Learning through the Use of an AI Tool
Pham, T. T. T., Nguyen, L. A. D., Dang, H. M., & Le, T. T. P. (2024) · Proceedings of the AsiaCALL International Conference · doi:10.54855/paic.23410 · Cited by 28
Clinical applications of artificial intelligence and machine learning‐based methods in inflammatory bowel disease
Cohen‐Mekelburg, S., Berry, S. K., Stidham, R. W., Zhu, J., & Waljee, A. K. (2021) · Journal of Gastroenterology and Hepatology · doi:10.1111/jgh.15405 · Cited by 22
When algorithms meet emotions: Understanding consumer satisfaction in AI companion applications
Silayach, N., Ray, R. K., Singh, N. K., Dash, D. P., & Singh, A. K. (2025) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2025.104298 · Cited by 21
Design an Intelligent System to automatically Tutor the Method for Solving Problems
Nguyen, H. D., Tran, D. A., Do, H. P., & Pham, V. T. (2020) · International Journal of Integrated Engineering · doi:10.30880/ijie.2020.12.07.024 · Cited by 21
Fridolin: participatory design and evaluation of a nutrition chatbot for older adults
Weber, P. P., Mahmood, F., Ahmadi, M., von Jan, V., Ludwig, T. G., & Wieching, R. (2023) · i-com · doi:10.1515/icom-2022-0042 · Cited by 21
A Primer on Seq2Seq Models for Generative Chatbots
Scotti, V., Sbattella, L., & Tedesco, R. (2023) · ACM Computing Surveys · doi:10.1145/3604281 · Cited by 29
Enhancing Translation Studies with Artificial Intelligence (AI): Challenges, Opportunities, and Proposals
SOYSAL, F. (2023) · Karamanoğlu Mehmetbey Üniversitesi Uluslararası Filoloji ve Çeviribilim Dergisi · doi:10.55036/ufced.1402649 · Cited by 21
Evolving Conversations: A Review of Chatbots and Implications in Natural Language Processing for Cultural Heritage Ecosystems
Suryanto, T. L. M., Wibawa, A. P., Hariyono, H., & Nafalski, A. (2023) · International Journal of Robotics and Control Systems · doi:10.31763/ijrcs.v3i4.1195 · Cited by 29
Designing Chatbots for Educational Leadership
Athavale, V. A., Selvakumar, P., Poorani, S., Anand, T., & Rathore, D. S. (2025) · IGI Global eBooks · doi:10.4018/979-8-3693-8734-4.ch008 · Cited by 21
Chatting with chatbots: Sign making in text-based human–computer interaction
Duncker, D. (2020) · Sign Systems Studies · doi:10.12697/sss.2020.48.1.05 · Cited by 21
Partner Matters! An Empirical Study on Fusing Personas for Personalized Response Selection in Retrieval-Based Chatbots
Gu, J.-C., Liu, H., Ling, Z.-H., Liu, Q., Chen, Z., & Zhu, X. (2021) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3404835.3462858 · Cited by 23
Robust Conversational AI with Grounded Text GenerationPreprint
Gao, J., Peng, B., Li, C., Li, J., Shayandeh, S., Lidén, L., & Shum, H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.03457 · Cited by 28
Self-citation
Personality-affected Emotion Generation in Dialog Systems
Wen, Z., Cao, J., Shen, J., Yang, R., Liu, S., & Sun, M. (2024) · ACM Transactions on Information Systems · doi:10.1145/3655616 · Cited by 22
The Design and Implementation of Language Learning Chatbot with XAI using Ontology and Transfer Learning
Shi, N., Zeng, Q. D., & Lee, R. (2020) · doi:10.5121/csit.2020.101124 · Cited by 20
Multi-Party Empathetic Dialogue Generation: A New Task for Dialog Systems
Zhu, L. Y., Zhang, Z., Wang, J., Wang, H., Wu, H., & Yang, Z. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.24 · Cited by 21
The role of knowledge graphs in chatbots
Rajabi, E., George, A. N., & Kumar, K. P. (2024) · The Electronic Library · doi:10.1108/el-03-2023-0066 · Cited by 20
Survey on dialogue systems including slavic languages
Wołk, K., Wołk, A., Wnuk, D. C., Grześ, T., & Skubis, I. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.11.076 · Cited by 21
An Empirical Investigation of Pre-Trained Transformer Language Models for Open-Domain Dialogue GenerationPreprint
Li, P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.04195 · Cited by 25
The Rise of Thinking Machines: A Review of Artificial Intelligence in Contemporary Communication
Gholami, M. J., & Abdwani, T. A. R. A. (2024) · Journal of Business Communication & Technology · doi:10.56632/bct.2024.3103 · Cited by 19
When, What, and how should generative artificial intelligence explain to Users?
Jang, S., Lee, H., Kim, Y., Lee, D., Shin, J., & Nam, J. (2024) · Telematics and Informatics · doi:10.1016/j.tele.2024.102175 · Cited by 20
Understanding consumer reactions to chatbot service failures: Evidence from a Wizard-of-Oz experiment
Cai, N., Heo, J., & Yan, J. (2025) · Acta Psychologica · doi:10.1016/j.actpsy.2025.104707 · Cited by 19
Towards retrieval-based conversational recommendation
Manzoor, A., & Jannach, D. (2022) · Information Systems · doi:10.1016/j.is.2022.102083 · Cited by 20
Enhancing User’s Self-Disclosure through Chatbot’s Co-Activity and Conversation Atmosphere Visualization
Pujiarti, R. N., Lee, B., & Yi, M. Y. (2022) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2022.2116414 · Cited by 19
Tutoring Postgraduate Students with an AI-Based Chatbot
Koivisto, M. (2023) · International Journal of Advanced Corporate Learning (iJAC) · doi:10.3991/ijac.v16i1.35437 · Cited by 19
Development of Conversational Artificial Intelligence for Pandemic Healthcare Query Support
Woo, W. L., Gao, B., Al-Nima, R. R. O., & Ling, B. W. (2020) · International Journal of Automation Artificial Intelligence and Machine Learning · doi:10.61797/ijaaiml.v1i1.35 · Cited by 20
Empathetic Response Generation via Emotion Cause Transition Graph
Qian, Y., Wang, B., Lin, T.-E., Zheng, Y., Zhu, Y., Zhao, D., Hou, Y., Wu, Y., & Li, Y. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10095652 · Cited by 19
OpenViDial 2.0: A Larger-Scale, Open-Domain Dialogue Generation Dataset with Visual ContextsPreprint
Wang, S., Meng, Y., Li, X., Sun, X., Ouyang, R., & Li, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.12761 · Cited by 19
ProtoChat
Choi, Y., Monserrat, T.-J. K. P., Park, J., Shin, H., Lee, N., & Kim, J. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3432924 · Cited by 18
PEDM: A Multi-task Learning Model for Persona-aware Emoji-embedded Dialogue Generation
Zhao, S., Jiang, H., Tao, H., Zha, R., Zhang, K., Xu, T., & Chen, E. (2022) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3571819 · Cited by 19
Toward Design Archetypes for Conversational Agent Personality
Lessio, N., & Morris, A. (2020) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc42975.2020.9283254 · Cited by 18
Empathetic Chatbot Enhancement and Development: A Literature Review
Wardhana, A. K., Ferdiana, R., & Hidayah, I. (2021) · IEEE International Conference on Artificial Intelligence and Mechatronics Systems (AIMS) · doi:10.1109/aims52415.2021.9466027 · Cited by 18
Touch in the Time of Corona
Steiner, H., & Veel, K. (2021) · De Gruyter eBooks · doi:10.1515/9783110744767 · Cited by 19
Application of Knowledge Distillation Based on Transfer Learning of ERNIE Model in Intelligent Dialogue Intention Recognition
Guo, S., & Wang, Q. (2022) · Sensors · doi:10.3390/s22031270 · Cited by 18
IAI MovieBot
Habib, J., Zhang, S., & Balog, K. (2020) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3340531.3417433 · Cited by 18
From Humanoid to Virtual Humans: A Systematic Literature Review of Avatar Marketing
Qu, Y., Lo, C. K. Y., & Baek, E. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2464889 · Cited by 20
Self-disclosure to AI: People provide personal information to AI and humans equivalently
Merwin, E. R., Hagen, A. C., Keebler, J. R., & Forbes, C. E. (2025) · Computers in Human Behavior Artificial Humans · doi:10.1016/j.chbah.2025.100180 · Cited by 17
Auto Response Generation in Online Medical Chat Services
Jahanshahi, H., Kazmi, S., & Çevik, M. (2022) · Journal of Healthcare Informatics Research · doi:10.1007/s41666-022-00118-x · Cited by 19
TegTok: Augmenting Text Generation via Task-specific and Open-world Knowledge
Tan, C.-H., Gu, J.-C., Tao, C., Ling, Z.-H., Xu, C., Hu, H., Geng, X., & Jiang, D. (2022) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2022.findings-acl.125 · Cited by 19
Chatbots in language learning: AI systems on the rise
Godwin‐Jones, R. (2022) · doi:10.14705/rpnet.2022.61.1446 · Cited by 17
Empathetic Language in LLMs under Prompt Engineering: A Comparative Study in the Legal Field
Zhang, Y., Rashidian, C., Brunswicker, S., Whitenack, D., & Linna, D. W. (2024) · Procedia Computer Science · doi:10.1016/j.procs.2024.10.204 · Cited by 18
A Cooperative Memory Network for Personalized Task-oriented Dialogue Systems with Incomplete User Profiles
Pei, J., Ren, P., & de Rijke, M. (2021) · International World Wide Web Conference (WWW) · doi:10.1145/3442381.3449843 · Cited by 17
Implementation of Chatbot on University Website Using RASA Framework
Fauzia, L., Hadiprakoso, R. B., & Girinoto (2021) · International Seminar on Research of Information Technology and Intelligent Systems (ISRITI) · doi:10.1109/isriti54043.2021.9702821 · Cited by 17
DAISY: An Implementation of Five Core Principles for Transparent and Accountable Conversational AI
Wahde, M., & Virgolin, M. (2022) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2022.2081762 · Cited by 17
Duplex Conversation: Towards Human-like Interaction in Spoken Dialogue Systems
Lin, T.-E., Wu, Y., Huang, F., Si, L., Sun, J., & Li, Y. (2022) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3534678.3539209 · Cited by 17
Inferring Speaking Styles from Multi-modal Conversational Context by Multi-scale Relational Graph Convolutional Networks
Li, J., Meng, Y., Wu, X., Wu, Z., Jia, J., Meng, H. M. L., Tian, Q., Wang, Y., & Wang, Y. (2022) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3503161.3547831 · Cited by 17
Hierarchical Reinforcement Learning for Open-Domain Dialog
Saleh, A., Jaques, N., Ghandeharioun, A., Shen, J. H., & Picard, R. W. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6400 · Cited by 16
Mental model shifts in human-LLM interactions
Schneider, J. (2025) · Journal of Intelligent Information Systems · doi:10.1007/s10844-025-00960-6 · Cited by 16
Chatbot or Chat-Blocker: Predicting Chatbot Popularity before Deployment
Gao, M., Liu, X., Xu, A., & Akkiraju, R. (2021) · Designing Interactive Systems Conference · doi:10.1145/3461778.3462147 · Cited by 16
An Interactional Account of Empathy in Human-Machine Communication
Concannon, S. J., Roberts, I., & Tomalin, M. (2023) · Human-Machine Communication · doi:10.30658/hmc.6.6 · Cited by 16
RT-LM: Uncertainty-Aware Resource Management for Real-Time Inference of Language Models
Li, Y., Li, Z., Yang, W. P., & Liu, C. (2023) · IEEE Real-Time Systems Symposium (RTSS) · doi:10.1109/rtss59052.2023.00023 · Cited by 20
Understanding College Students’ Satisfaction With ChatGPT: An Exploratory And Predictive Machine Learning Approach Using Feature Engineering
Pabreja, K., & Pabreja, N. (2024) · MIER Journal of Educational Studies Trends & Practices · doi:10.52634/mier/2024/v14/i1/2568 · Cited by 17
Time to Transfer: Predicting and Evaluating Machine-Human Chatting Handoff
Liu, J., Gao, Z., Kang, Y., Jiang, Z., He, G., Sun, C., Liu, X., & Lu, W. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i7.16731 · Cited by 19
Deep Learning for Dialogue Systems: Chit-Chat and Beyond
Yan, R., Li, J., & Zhou, Y. (2022) · Foundations and Trends® in Information Retrieval · doi:10.1561/1500000083 · Cited by 17
DHF-Net: A hierarchical feature interactive fusion network for dialogue emotion recognition
Gan, C., Yang, Y., Zhu, Q., Jain, D. K., & Štruc, V. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118525 · Cited by 16
Automated Interactive Domain-Specific Conversational Agents that Understand Human Dialogs
Zeng, Y., Rajasekharan, A., Padalkar, P., Basu, K., Arias, J., & Gupta, G. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-52038-9_13 · Cited by 17
Graph-Grounded Goal Planning for Conversational Recommendation
Liu, Z., Zhou, D., Liu, H., Wang, H., Niu, Z.-Y., Wu, H., Che, W., Liu, T., & Xiong, H. (2022) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2022.3147210 · Cited by 16
LiveChat: A Large-Scale Personalized Dialogue Dataset Automatically Constructed from Live Streaming
Gao, J., Lian, Y., Zhou, Z., Fu, Y., & Wang, B. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.858 · Cited by 28
Self-citation
Behaviors and Perceptions of Human-Chatbot Interactions Based on Top Active Users of a Commercial Social Chatbot
Chin, H., Zhunis, A., & Cha, M. (2024) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3687022 · Cited by 18
Say What I Want: Towards the Dark Side of Neural Dialogue ModelsPreprint
Liu, H., Derr, T., Liu, Z., & Tang, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.06044 · Cited by 16
Leveraging Artificial Intelligence–Powered Chatbots for Nonprofit Organizations: Examining the Antecedents and Outcomes of Chatbot Trust and Social Media Engagement
Cheng, Y., & Wang, Y. (2025) · Journal of Philanthropy · doi:10.1002/nvsm.70013 · Cited by 14
Examining the link between distressing life events, social media distress disclosure, and perceived stress: A moderated mediation model
Chu, T. H., & Jiang, L. (2023) · Computers in Human Behavior · doi:10.1016/j.chb.2023.108018 · Cited by 14
Generation-based vs. Retrieval-based Conversational Recommendation: A User-Centric Comparison
Manzoor, A., & Jannach, D. (2021) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/3460231.3475942 · Cited by 18
Response-act Guided Reinforced Dialogue Generation for Mental Health Counseling
Srivastava, A., Pandey, I., Akhtar, M. S., & Chakraborty, T. (2023) · International World Wide Web Conference (WWW) · doi:10.1145/3543507.3583380 · Cited by 14
There is no Artificial General IntelligencePreprint
Landgrebe, J., & Smith, B. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.05833 · Cited by 21
Empathy by Design: Reframing the Empathy Gap Between AI and Humans in Mental Health Chatbots
Howcroft, A., & Blake, H. (2025) · Information · doi:10.3390/info16121074 · Cited by 13
Transfer Hierarchical Attention Network for Generative Dialog System
Zhang, X., & Yang, Q. (2019) · Machine Intelligence Research · doi:10.1007/s11633-019-1200-0 · Cited by 13
An Emotional Comfort Framework for Improving User Satisfaction in E-Commerce Customer Service Chatbots
Song, S., Wang, C., Chen, H., & Chen, H. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-industry.17 · Cited by 13
Beyond Text and Speech in Conversational Agents: Mapping the Design Space of Avatars
Rashik, M., Jasim, M., Kucher, K., Sarvghad, A., & Mahyar, N. (2024) · Designing Interactive Systems Conference · doi:10.1145/3643834.3661563 · Cited by 16
Marrying Dialogue Systems with Data Visualization: Interactive Data Visualization Generation from Natural Language Conversations
Song, Y., Zhao, X., & Wong, R. C.-W. (2024) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3637528.3671935 · Cited by 13
ODO: Design of Multimodal Chatbot for an Experiential Media System
Bhushan, R., Kulkarni, K., Pandey, V. K., Rawls, C., Mechtley, B., Jayasuriya, S., & Ziegler, C. (2020) · Multimodal Technologies and Interaction · doi:10.3390/mti4040068 · Cited by 13
Småprat: DialoGPT for Natural Language Generation of Swedish Dialogue by Transfer Learning
Adewumi, T., Brännvall, R., Abid, N., Pahlavan, M., Sabah, S., Liwicki, F., & Liwicki, M. (2022) · Proceedings of the Northern Lights Deep Learning Workshop · doi:10.7557/18.6231 · Cited by 15
User Satisfaction with an AI-Enabled Customer Relationship Management Chatbot
Sohail, M., Mohsin, Z., & Khaliq, S. (2021) · Communications in computer and information science · doi:10.1007/978-3-030-90176-9_36 · Cited by 13
DQ-HGAN: A heterogeneous graph attention network based deep Q-learning for emotional support conversation generation
Li, G., Wu, M., Wang, C., & Liu, Z. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.111201 · Cited by 13
Generating Diversified Comments via Reader-Aware Topic Modeling and Saliency Detection
Wang, W., Li, P., & Zheng, H.-T. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i16.17647 · Cited by 14
Becoming human: How perceived anthropomorphism and authenticity influence romantic attractiveness of chat agents
Ma, S., & Koike, M. (2025) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2025.2481211 · Cited by 12
Mind the Gap! Choice Independence in Using Multilingual LLMs for Persuasive Co-Writing Tasks in Different Languages
Biswas, S., Erlei, A., & Gadiraju, U. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713201 · Cited by 17
'No, not that voice again!': Engaging Older Adults in Design of Anthropomorphic Voice Assistants
Pradhan, A., Erete, S. L., Chopra, S., Upadhyay, P., Sule, O., & Lazar, A. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3711039 · Cited by 15
Adversarial Knowledge Distillation Based Biomedical Factoid Question Answering
Bai, J., Yin, C., Zhang, J., Wang, Y., Dong, Y., Rong, W., & Xiong, Z. (2022) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2022.3161032 · Cited by 12
Facilitation of Customer Empathy: The Effect of Robot Apology on Customer Reaction Following a Service Failure
Shen, W., & Wang, Y. (2022) · Journal of Marketing Development and Competitiveness · doi:10.33423/jmdc.v16i2.5254 · Cited by 12
Interactive Search on the Web: The Story So Far
Aghaei, S., Angele, K., Huaman, E., Bushati, G., Schiestl, M., & Fensel, A. (2022) · Information · doi:10.3390/info13070324 · Cited by 14
The City as a Personal Assistant
Acer, U. G., den Broeck, M. V., Min, C., Dasari, M., & Kawsar, F. (2022) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3534573 · Cited by 12
SOSYAL BİLİMLER ARAŞTIRMALARI İÇİN CHATGPT POTANSİYELİNİN AÇIĞA ÇIKARILMASI: UYGULAMALAR, ZORLUKLAR VE GELECEK YÖNELİMLER
AŞKUN, V. (2023) · Erciyes Akademi · doi:10.48070/erciyesakademi.1281544 · Cited by 13
Towards Boosting the Open-Domain Chatbot with Human Feedback
Lu, H. C., Bao, S., He, H., Wang, F., Wu, H., & Wang, H. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.224 · Cited by 15
EmoChat: Bringing Multimodal Emotion Detection to Mobile Conversation
Chong, L., Jin, M., & He, Y. (2019) · International Conference on Big Data Computing and Communications (BigCom) · doi:10.1109/bigcom.2019.00037 · Cited by 12
DiSCoL: Toward Engaging Dialogue Systems through Conversational Line Guided Response Generation
Ghazarian, S., Liu, Z., Chakrabarty, T., Ma, X., Galstyan, A., & Peng, N. (2021) · doi:10.18653/v1/2021.naacl-demos.4 · Cited by 12
Coffee With a Hint of Data: Towards Using Data-Driven Approaches in Personalised Long-Term Interactions
Irfan, B., Hellou, M., & Belpaeme, T. (2021) · Frontiers in Robotics and AI · doi:10.3389/frobt.2021.676814 · Cited by 13
A Survey of Document Grounded Dialogue Systems (DGDS)Preprint
Ma, L., Zhang, W., Li, M., & Liu, T. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.13818 · Cited by 15

Authors: Zhou, L., Gao, J., Li, D., & Shum, H.-Y.
Affiliations: Microsoft (United States) (US)
ORCID: Gao, Jianfeng
Li, Di
Shum, Heung-Yeung
Venue: Computational Linguistics
Publisher: MIT Press
Published: 2020-03
Volume: 46
Issue: 1
Pages: 53-93
ISSN: 0891-2017 (ISSN-L)
1530-9312
DOI: 10.1162/coli_a_00368
OpenAlex: W2906579211
Semantic Scholar: 66b7d31527f980bb2eecc23629f08ba6037facc4
Cited by: 777
References: 66
Citations by year: 2026: 97 · 2025: 109 · 2024: 106 · 2023: 113 · 2022: 120 · 2021: 112 · 2020: 94
FWCI: 43.11
Type: journal article
Language: en
Open access: diamond
doi.org
Licence: cc-by-nc-nd
Locations: Computational Linguistics (publishedVersion, cc-by-nc-nd)
arXiv (Cornell University) (submittedVersion)
Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
arXiv (Cornell University)
Funders: Microsoft Research
Microsoft Research Asia
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
Social Sciences › Business, Management and Accounting › Marketing › AI in Service Interactions
Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
Keywords: social chatbots, Markov decision process, dialogue management, user engagement, emotion recognition, user intent understanding

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

A language model drew the claims from the paper on 2026-10-08, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).

A second model labelled each claim on 2026-10-08 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

The Design and Implementation of XiaoIce, an Empathetic Social Chatbot

Abstract

Overview

AI snapshot

TL;DR

Key findings

Methods

Limitations

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks