Dialogue Natural Language Inference

Adam: A Method for Stochastic Optimization

Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946

Glove: Global Vectors for Word Representation

Pennington, J., Socher, R., & Manning, C. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1162 · Cited by 34,071

GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding

Wang, A., Singh, A., Michael, J., Hill, F., Levy, O., & Bowman, S. (2018) · Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP · doi:10.18653/v1/w18-5446 · Cited by 4,222

A large annotated corpus for learning natural language inference

Bowman, S. R., Angeli, G., Potts, C., & Manning, C. D. (2015) · Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d15-1075 · Cited by 3,377

A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference

Williams, A., Nangia, N., & Bowman, S. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · doi:10.18653/v1/n18-1101 · Cited by 3,342

The No-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte CarloPreprint

Homan, M. D., & Gelman, A. (2014) · arXiv (Cornell University) · Cited by 3,078

Supervised Learning of Universal Sentence Representations from Natural Language Inference Data

Conneau, A., Kiela, D., Schwenk, H., Barrault, L., & Bordes, A. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d17-1070 · Cited by 2,049

The PASCAL Recognising Textual Entailment Challenge

Dagan, I., Glickman, O., & Magnini, B. (2006) · Lecture notes in computer science · doi:10.1007/11736790_9 · Cited by 1,674

Untitled work

(2018) · International Conference on Learning Representations · Cited by 1,621

A Neural Conversational Model

Vinyals, O., & Le, Q. (2015) · arXiv · doi:10.48550/arxiv.1506.05869 · Cited by 1,400

Let the Model Decide its Curriculum for Multitask Learning

Varshney, N., & Baral, S. M. A. C. (2022) · Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo) · doi:10.18653/v1/2022.deeplo-1.13 · Cited by 5

RPTCS: A Reinforced Persona-aware Topic-guiding Conversational System

Ahmad, Z., Mishra, K., Ekbal, A., & Bhattacharyya, P. (2023) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2023.eacl-main.253 · Cited by 1

DialGuide: Aligning Dialogue Model Behavior with Developer Guidelines

Gupta, P., Liu, Y., Jin, D., Hedayatnia, B., Gella, S., Liu, S., Lange, P. L., Hirschberg, J., & Hakkani-Tür, D. Z. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.935 · Cited by 1

Analyzing natural language inference from a rigorous point of view

Salvatore, F. D. S. (2020) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/t.45.2020.tde-05012021-151600 · Cited by 1

Learning to Predict Persona Information forDialogue Personalization without Explicit Persona DescriptionPreprint

Zhou, W., Li, Q., & Li, C. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.15093 · Cited by 4

Testing the Reasoning Power for NLI Models with Annotated Multi-perspective Entailment Dataset

Yu, D., Liu, L., Chen, Y., & Li, C. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32381-3_2

Public Self-consciousness for Endowing Dialogue Agents with Consistent PersonaPreprint

Kim, H., Kim, B., & Kim, G. (2020) · arXiv (Cornell University)

Interviewer-Candidate Role Play: Towards Developing Real-World NLP SystemsPreprint

Varshney, N., Mishra, S. R., & Baral, C. R. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.00315

Définition et détection des incohérences du système dans les dialogues orientés tâche. (We present experiments on automatically detecting inconsistent behavior of task-oriented dialogue systems from the context)

Schaub, L.-P., Hudeček, V., Stancl, D., Dušek, O., & Paroubek, P. (2021)

Taming the beast : learning to control neural conversational models

Madotto, A. (2021) · doi:10.14711/thesis-991012980222103412 · Cited by 1

Proxy Indicators for the Quality of Open-domain Dialogues

Nedelchev, R., Lehmann, J., & Usbeck, R. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.618

Profile Consistency Discrimination

Zhou, J. L., Sun, X., Wang, S., Jia, J., Li, H., Wang, M., & Li, S. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-11217-1_14

Neural Network Models For User Attribute Extraction From Dialogues

Dang, S., & Minh, P. Q. (2022) · International Conference on Speech Database and Assessments · doi:10.1109/o-cocosda202257103.2022.9997870

Bibliographie

Layer-Wagner, T., Kriglstein, S., Schrank, B., Frankus, E., & Häufler, I. (2023) · Digitale Gesellschaft · doi:10.14361/9783839465646-009

Nachwort

Layer-Wagner, T., Kriglstein, S., Schrank, B., Frankus, E., & Häufler, I. (2023) · Digitale Gesellschaft · doi:10.14361/9783839465646-008

Kapitel 1: Entstehung des Projekts im Ideen-Lab

Layer-Wagner, T., Kriglstein, S., Schrank, B., Frankus, E., & Häufler, I. (2023) · Digitale Gesellschaft · doi:10.14361/9783839465646-002

Kapitel 6: Gender und Diversität

Layer-Wagner, T., Kriglstein, S., Schrank, B., Frankus, E., & Häufler, I. (2023) · Digitale Gesellschaft · doi:10.14361/9783839465646-007

Kapitel 4: Technische Entwicklung

Layer-Wagner, T., Kriglstein, S., Schrank, B., Frankus, E., & Häufler, I. (2023) · Digitale Gesellschaft · doi:10.14361/9783839465646-005

Kapitel 5: Testung und Evaluation des Prototyps

Layer-Wagner, T., Kriglstein, S., Schrank, B., Frankus, E., & Häufler, I. (2023) · Digitale Gesellschaft · doi:10.14361/9783839465646-006

Kapitel 3: Ko-Kreation

Layer-Wagner, T., Kriglstein, S., Schrank, B., Frankus, E., & Häufler, I. (2023) · Digitale Gesellschaft · doi:10.14361/9783839465646-004

Kapitel 2: Soziale Kompetenzen im Beruf

Layer-Wagner, T., Kriglstein, S., Schrank, B., Frankus, E., & Häufler, I. (2023) · Digitale Gesellschaft · doi:10.14361/9783839465646-003

Einleitung

Layer-Wagner, T., Kriglstein, S., Schrank, B., Frankus, E., & Häufler, I. (2023) · Digitale Gesellschaft · doi:10.14361/9783839465646-001

Inhalt

Layer-Wagner, T., Kriglstein, S., Schrank, B., Frankus, E., & Häufler, I. (2023) · Digitale Gesellschaft · doi:10.14361/9783839465646-toc

Frontmatter

Layer-Wagner, T., Kriglstein, S., Schrank, B., Frankus, E., & Häufler, I. (2023) · Digitale Gesellschaft · doi:10.14361/9783839465646-fm

»Virtual Skills Lab« - Transdisziplinäres Forschen zur Vermittlung sozialer Kompetenzen im digitalen Wandel

Layer-Wagner, T., Kriglstein, S., Schrank, B., Frankus, E., & Häufler, I. (2023) · transcript Verlag eBooks · doi:10.1515/9783839465646

Bibliographie

Layer-Wagner, T., Kriglstein, S., Schrank, B., Frankus, E., & Häufler, I. (2023) · transcript Verlag eBooks · doi:10.1515/9783839465646-009

PAGE: Endowing Dialogue Agents with Personalized Characters via Persona Perception

Li, J., Xu, F., Wang, Y., Liu, P., Zhu, Z., & Wang, J. (2022) · IEEE Smart World Congress (SWC) · doi:10.1109/smartworld-uic-atc-scalcom-digitaltwin-pricomp-metaverse56740.2022.00269

Please don't answer out of context: Personalized Dialogue Generation Fusing Persona and Context

Wang, F., Yin, Y., Huang, F., & Wu, K. (2023) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn54540.2023.10191649 · Cited by 1

DKAF: KB Arbitration for Learning Task-Oriented Dialog Systems with Dialog-KB Inconsistencies

Saley, V. V., Das, R. J., Raghu, D., & Mausam, M. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.744

Unsupervised Contradiction Detection using Sentence Transformations

Schumann, G., & Gómez, J. M. (2023) · doi:10.1109/icnlp58431.2023.00065

Natural Language Inference by Integrating Deep and Shallow Representations with Knowledge Distillation

Chen, P.-C., Ma, H.-S., & Huang, J.-W. (2023) · IEEE International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa60987.2023.10302536

AutoReply: Detecting Nonsense in Dialogue with Discriminative Replies

Shi, W., Dinan, E., Renduchintala, A., Fried, D., Jacob, A. P., Zhou, Y., & Lewis, M. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.23

CESAR: Automatic Induction of Compositional Instructions for Multi-turn Dialogs

Aksu, T., Hazarika, D., Mehri, S., Kim, S., Hakkani-Tür, D. Z., Liu, Y., & Namazifar, M. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.717 · Cited by 1

Development of Character-like Chatbot with Role Play-Based Question Answering and a Large Language Model

Mitsuda, K., Higashinaka, R., Li, T., Sugiyama, H., Mizukami, M., Nakamura, R., Adachi, N., Kawabata, H., Yoshida, S., & Kinebuchi, T. (2023) · Journal of Natural Language Processing · doi:10.5715/jnlp.30.1172

Who Said That?

Ribeiro, R., Carvalho, J. P., & Coheur, L. (2023) · ACM International Conference on Intelligent Virtual Agents (IVA) · doi:10.1145/3570945.3607319

PICK: Polished & Informed Candidate Scoring for Knowledge-Grounded Dialogue Systems

Wilie, B., Xu, Y., Chung, W., Cahyawijaya, S., Lovenia, H., & Fung, P. (2023) · International Joint Conference on Natural Language Processing and Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (IJCNLP-AACL) · doi:10.18653/v1/2023.ijcnlp-main.63 · Cited by 1

Leveraging rule-based model and machine learning transformer for mining aspect-based financial opinions in colloquial language

Chu, C. C. F., So, R., Kwong, E. K.-L., & Chan, A. (2023) · International Conference on Software and e-Business (ICSEB) · doi:10.1145/3641067.3641075 · Cited by 2

Exploiting Persona Perception for Diverse Generation from Limited Personalized Data

Zhang, C., Zha, D., Wang, L., Mu, N., & Xu, F. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-5678-0_19

Transformer model with external token memories and attention for PersonaChat

Sun, T., & Fujita, K. (2025) · Scientific Reports · doi:10.1038/s41598-025-98850-y

Multi-hop ContraDial: Multi-hop Reasoning for Contradiction Detection in Dialogue

Lin, T., & Rao, D. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-9994-0_32

Is it Enough to Ask Questions? Dialogue Evaluation through Question Answering and Generation

Vilaça, L., & Viana, P. (2025) · doi:10.1145/3746274.3760399

PQMT: Position-aware Network and Qualifier-Interactive Attention for multi-task hyper-relation extraction

Hang, T., Ding, H., Tang, Q., & Feng, J. (2025) · Information Processing & Management · doi:10.1016/j.ipm.2025.104453

Accessible Six-DOF Clay-Printing Platform Using a 3D-Printed Robotic Arm

Čabaravdić, M., & Sarajlić, A. (2026) · AI Computer Science and Robotics Technology · doi:10.5772/acrt.20250181

Engagingness in Open-Domain Dialogue Systems: A Systematic Survey of Datasets, Metrics and Methods

Mitra, E., Ferron, A., & Agrawal, A. (2026) · AI Computer Science and Robotics Technology · doi:10.5772/acrt20250081

Investigating User-Side Answer Errors and Large Language Model Awareness in Goal-Oriented Conversations

Mirabi, S., Ofoghi, B., Yearwood, J. L., & Mollá, D. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-25035-3_12

EMP: Integrating Emotion Reasoning, Memory Structuring, and Persona Refinement for Long-Term Personalized Dialogue Generation

Ding, C., & Zhu, W. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-3520-9_15

Category-Conditioned Persona Retrieval for Multi-session Dialogue: A Method and a Gold-Annotation-Compatible Retrieval Metric

Zaitsev, K. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-39150-6_14

FHRI: Scenario-Aware Reliability Scoring for Hallucination and Contradiction Detection in Financial LLM Responses

Teh, Y. C., & Mallikarachchi, D. (2026) · IEEE International Conference on Signal and Image Processing Applications (ICSIPA) · doi:10.1109/icsipa69622.2026.11720077

Advancing depression detection with role-playing LLMs and synthetic psychological interviews

Liu, Y., Hu, J., Chen, Y., & Zhang, Y. (2026) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-026-03337-5

Integrating Deep Learning and Knowledge Graphs for Natural Language Inference

Gianoulari, E., & Hatzilygeroudis, I. (2026) · Electronics · doi:10.3390/electronics15204764

Generating Persona Consistent Dialogues by Exploiting Natural Language Inference

Song, H., Zhang, W., Hu, J., & Liu, T. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6417 · Cited by 76

Will I Sound Like Me? Improving Persona Consistency in Dialogues through Pragmatic Self-Consciousness

Kim, H., Kim, B., & Kim, G. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.65 · Cited by 55

Enhancing Self-Consistency and Performance of Pre-Trained Language Models through Natural Language Inference

Mitchell, E., Noh, J. J., Li, S., Armstrong, W., Agarwal, A., Liu, P., Finn, C., & Manning, C. D. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.115 · Cited by 34

Profile Consistency Discrimination

Zhou, J. L., Sun, X., Wang, S., Jia, J., Li, H., Wang, M., & Li, S. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-11217-1_14

Exploring the Factual Consistency in Dialogue Comprehension of Large Language Models

She, S., Huang, S., Wang, X., Zhou, Y., & Chen, J. (2024) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2024.naacl-long.338 · Cited by 1