The DOI matches its Crossref record; 72 of the 72 references it cites were found in a registry or an index.
Refocusing on Relevance: Personalization in NLG
Open accessAbstract
Many NLG tasks such as summarization, dialogue response, or open domain question answering focus primarily on a source text in order to generate a target response.This standard approach falls short, however, when a user's intent or context of work is not easily recoverable based solely on that source texta scenario that we argue is more of the rule than the exception.In this work, we argue that NLG systems in general should place a much higher level of emphasis on making use of additional context, and suggest that relevance (as used in Information Retrieval) be thought of as a crucial tool for designing user-oriented text-generating tasks.We further discuss possible harms and hazards around such personalization, and argue that value-sensitive design represents a crucial path forward through these challenges.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
Personalized AI applications, including personalized AI writing assistance, are becoming more common and readily accessible.
“Meanwhile, personalized AI applications-including personalized AI writing assistance-are becoming more common and readily accessible [9,14,27,65].”
From 'It was 80% me, 20% AI': Seeking Authenticity in Co-Writing with Large Language Models (2025) · cites this paper as [14]
The Potential Harms from Textual Personalization section (page unavailable) refers to the widespread deployment of personalized information systems, supporting the broad point that such applications are in use. The paper does not establish that personalized AI applications are becoming more common or readily accessible, specifically including writing assistance. (LLM, conf=0.78)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
An Economic Theory of Democracy.
Banfield, E. C., & Downs, A. J. (1958) · Midwest Journal of Political Science · doi:10.2307/2109186 · Cited by 18,558
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939778 · Cited by 17,555
MIMIC-III, a freely accessible critical care database
Johnson, A. E. W., Pollard, T. J., Shen, L., Lehman, L.-W. H., Feng, M., Ghassemi, M., Moody, B., Szolovits, P., Anthony Celi, L., & Mark, R. G. (2016) · Scientific Data · doi:10.1038/sdata.2016.35 · Cited by 8,141
Confirmation Bias: A Ubiquitous Phenomenon in Many Guises
Nickerson, R. S. (1998) · Review of General Psychology · doi:10.1037/1089-2680.2.2.175 · Cited by 7,267
Federated learning: Challenges, methods, and future directions
Li, T., Sahu, A. K., Talwalkar, A., & Smith, V. (2020) · IEEE Signal Processing Magazine · doi:10.1109/msp.2020.2975749 · Cited by 6,210
Get To The Point: Summarization with Pointer-Generator Networks
See, A., Liu, P. J., & Manning, C. D. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p17-1099 · Cited by 3,775
Decentralizing Privacy: Using Blockchain to Protect Personal Data
Zyskind, G., Nathan, O., & Pentland, A. '. (2015) · 2015 IEEE Security and Privacy Workshops · doi:10.1109/spw.2015.27 · Cited by 2,617
Prefix-Tuning: Optimizing Continuous Prompts for Generation
Li, X. L., & Liang, P. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · doi:10.18653/v1/2021.acl-long.353 · Cited by 2,473
Filter Bubbles, Echo Chambers, and Online News Consumption
Flaxman, S., Goel, S., & Rao, J. M. (2016) · Public Opinion Quarterly · doi:10.1093/poq/nfw006 · Cited by 1,992
Deep Reinforcement Learning for Dialogue Generation
Li, J., Monroe, W., Ritter, A., Jurafsky, D., Galley, M., & Gao, J. (2016) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d16-1127 · Cited by 1,393
Personalizing Dialogue Agents: I have a dog, do you have pets too?
Zhang, S., Dinan, E., Urbanek, J., Szlam, A., Kiela, D., & Weston, J. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1205 · Cited by 1,177
Beyond the comparator model: A multifactorial two-step account of agency
Synofzik, M., Vosgerau, G., & Newen, A. (2007) · Consciousness and Cognition · doi:10.1016/j.concog.2007.03.010 · Cited by 1,135
User Modeling via Stereotypes*
Rich, E. A. (1979) · Cognitive Science · doi:10.1207/s15516709cog0304_3 · Cited by 1,020
Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems
Wen, T.-H., Gašić, M., Mrkšić, N., Su, P.-H., Vandyke, D., & Young, S. J. (2015) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d15-1199 · Cited by 968
A Persona-Based Neural Conversation Model
Li, J., Galley, M., Brockett, C., Spithourakis, G., Gao, J., & Dolan, B. (2016) · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p16-1094 · Cited by 898
RELEVANCE: A review of and a framework for the thinking on the notion in information science
Saračević, T. (1975) · Journal of the American Society for Information Science · doi:10.1002/asi.4630260604 · Cited by 863
WikiQA: A Challenge Dataset for Open-Domain Question Answering
Yang, Y., Yih, W.-T., & Meek, C. (2015) · Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d15-1237 · Cited by 829
Electoral Competition with Informed and Uninformed Voters
Baron, D. P. (1994) · American Political Science Review · doi:10.2307/2944880 · Cited by 661
Towards a Human-like Open-Domain Chatbot
Adiwardana, D., Luong, M.-T., So, D. R., Hall, J., Fiedel, N., Thoppilan, R., Yang, Z., Kulshreshtha, A., Nemade, G., Lu, Y., et al. (2020) · arXiv · doi:10.48550/arxiv.2001.09977 · Cited by 614
The National COVID Cohort Collaborative (N3C): Rationale, design, infrastructure, and deployment
Haendel, M. A., Chute, C. G., Bennett, T. D., Eichmann, D. A., Guinney, J., Kibbe, W. A., Payne, P. R. O., Pfaff, E. R., Robinson, P. N., Saltz, J. H., et al. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa196 · Cited by 553
Newsroom: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategies
Grusky, M., Naaman, M., & Artzi, Y. (2018) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n18-1065 · Cited by 538
The concept of relevance in IR
Borlund, P. (2003) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.10286 · Cited by 489
A re-examination of relevance: toward a dynamic, situational definition∗
Schamber, L., Eisenberg, M. B., & Nilan, M. S. (1990) · Information Processing & Management · doi:10.1016/0306-4573(90)90050-c · Cited by 429
Why We Need New Evaluation Metrics for NLG
Novikova, J., Dušek, O., Cercas Curry, A., & Rieser, V. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d17-1238 · Cited by 422
Assessing gender bias in machine translation: a case study with Google Translate
Prates, M. O. R., Avelar, P. H., & Lamb, L. C. (2020) · Neural Computing and Applications · doi:10.1007/s00521-019-04144-6 · Cited by 410
The Effect of Information on Voter Turnout: Evidence from a Natural Experiment
Lassen, D. D. (2004) · American Journal of Political Science · doi:10.1111/j.0092-5853.2005.00113.x · Cited by 335
MoEL: Mixture of Empathetic Listeners
Lin, Z., Madotto, A., Shin, J., Xu, P., & Fung, P. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1012 · Cited by 256
Truth Is a Lie: Crowd Truth and the Seven Myths of Human Annotation
Aroyo, L. M., & Welty, C. (2015) · AI Magazine · doi:10.1609/aimag.v36i1.2564 · Cited by 248
Predictive biases in natural language processing models: A conceptual framework and overview
Shah, D. S., Schwartz, H. A., & Hovy, D. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.468 · Cited by 241
Auto-summarization of audio-video presentations
He, L.-W., Sanocki, E., Gupta, A., & Grudin, J. T. (1999) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/319463.319691 · Cited by 224
MSMO: Multimodal Summarization with Multimodal Output
Zhu, J., Li, H., Liu, T., Zhou, Y. P., Zhang, J., & Zong, C. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1448 · Cited by 188
Personalizing Dialogue Agents via Meta-Learning
Madotto, A., Lin, Z., Wu, C.-S., & Fung, P. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1542 · Cited by 183
Controlling User Perceptions of Linguistic Style: Trainable Generation of Personality Traits
Mairesse, F., & Walker, M. A. (2011) · Computational Linguistics · doi:10.1162/coli_a_00063 · Cited by 174
TLDR: Extreme Summarization of Scientific Documents
Cachola, I., Lo, K., Cohan, A., & Weld, D. (2020) · Findings of the Association for Computational Linguistics: EMNLP 2020 · doi:10.18653/v1/2020.findings-emnlp.428 · Cited by 169 · cited 1×
“…of multiple reference outputs, as in such cases an implicit assumption is that the references should typically exhibit minor linguistic variation, rather than summaries that vary substantially in their con- tents (Cachola et al., 2020; See et al., 2017; Grusky et al., 2018; Harman and Over, 2004).”
BackgroundSummarization Evaluation: An Overview.
Mani, I. (2001) · Cited by 156
Value sensitive design : shaping technology with moral imagination
Friedman, B., & Hendry, D. G. (2019) · Cited by 156
The ARPA MT Evaluation Methodologies: Evolution, Lessons, and Future Approaches
White, J. S., O'Connell, T. A., & O'Mara, F. E. (1994) · Conference of the Association for Machine Translation in the Americas · Cited by 156
Towards Persona-Based Empathetic Conversational Models
Zhong, P., Zhang, C., Wang, H., Liu, Y., & Miao, C. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.531 · Cited by 142
Knowledge-aware Multimodal Dialogue Systems
Liao, L., Ma, Y., He, X., Hong, R., & Chua, T. (2018) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3240508.3240605 · Cited by 134
Analyzing the Impact of Filter Bubbles on Social Network Polarization
Chitra, U., & Musco, C. (2020) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3336191.3371825 · Cited by 134
Generation and evaluation of user tailored responses in multimodal dialogue
Walker, M. A., Whittaker, S. J., Stent, A., Maloor, P., Moore, J., Johnston, M., & Vasireddy, G. (2004) · Cognitive Science · doi:10.1016/j.cogsci.2004.06.002 · Cited by 125
Walters, S. (2010) · Technical Communication Quarterly · doi:10.1080/10572252.2010.502090 · Cited by 119
MODELING THE USER IN NATURAL LANGUAGE SYSTEMS
Kass, R., & Finin, T. (1988) · Cited by 117
Paraphrasing with Large Language Models
Witteveen, S., & Andrews, M. (2019) · Workshop on Neural Generation and Translation (WNGT) · doi:10.18653/v1/d19-5623 · Cited by 117
Data Voids: Where Missing Data Can Easily Be Exploited
boyd, D., & Golebiewski, M. (2018) · Cited by 107
Automatic summarising: factors and directionsPreprint
Jones, K. S. (1998) · arXiv (Cornell University) · doi:10.48550/arxiv.cmp-lg/9805011 · Cited by 103
Balancing access to health data and privacy: a review of the issues and approaches for the future.
Lane, J., & Schur, C. (2010) · Health Services Research · doi:10.1111/j.1475-6773.2010.01141.x · Cited by 102
Work tasks and socio‐cognitive relevance: A specific example
Hjørland, B., & Christensen, F. S. (2002) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.10132 · Cited by 89
McKeown, K. R., Passonneau, R. J., Elson, D. K., Nenkova, A., & Hirschberg, J. (2005) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/1076034.1076072 · Cited by 77
Evaluation-as-a-Service for the Computational Sciences
Hopfgartner, F., Hanbury, A., Müller, H., Eggel, I., Balog, K., Brodt, T., Cormack, G. V., Lin, J., Kalpathy–Cramer, J., Kando, N., et al. (2018) · Journal of Data and Information Quality · doi:10.1145/3239570 · Cited by 72
Crowd Truth: Harnessing disagreement in crowdsourcing a relation extraction gold standard
Aroyo, L. M. (2013) · Figshare · doi:10.6084/m9.figshare.679997.v1 · Cited by 69
Evaluation in the context of natural language generation
Mellish, C., & Dale, R. (1998) · Computer Speech & Language · doi:10.1006/csla.1998.0106 · Cited by 68
Crowd Truth: Harnessing disagreement in crowdsourcing a relation extraction gold standard
Aroyo, L. M. (2013) · Figshare · doi:10.6084/m9.figshare.679997 · Cited by 68
Returning the N to NLP: Towards Contextually Personalized Classification Models
Flek, L. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.700 · Cited by 59
Kopp, S., & Krämer, N. C. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.580955 · Cited by 58
The Effects of Human Variation in DUC Summarization Evaluation
Harman, D. K., & Over, P. (2004) · Cited by 48
A Proposal for Task-based Evaluation of Text Summarization Systems
Hand, T. F. (1997) · Cited by 48
Anticipating Safety Issues in E2E Conversational AI: Framework and Tooling
Dinan, E., Abercrombie, G., Bergman, A. S., Spruit, S., Hovy, D., Boureau, Y.-L., & Rieser, V. (2021) · arXiv · doi:10.48550/arxiv.2107.03451 · Cited by 44
A Distributional Approach to Controlled Text Generation
Khalifa, M., Elsahar, H., & Dymetman, M. (2020) · arXiv · doi:10.48550/arxiv.2012.11635 · Cited by 39
MacAvaney, S., Mittu, A., Coppersmith, G., Leintz, J., & Resnik, P. (2021) · Workshop on Computational Linguistics and Clinical Psychology (CLPsych) · doi:10.18653/v1/2021.clpsych-1.7 · Cited by 38
Interrogating LeftTube: ContraPoints and the Possibilities of Critical Media Praxis on YouTube
Maddox, J., & Creech, B. (2020) · Television & New Media · doi:10.1177/1527476420953549 · Cited by 36
Corrigan, H. B., Crăciun, G., & Powell, A. M. (2014) · Marketing Education Review · doi:10.2753/mer1052-8008240206 · Cited by 32
Searching for Alternative Facts
Tripodi, F. (2018) · Cited by 30
Text Simplification from Professionally Produced Corpora
Scarton, C., Paetzold, G. H., & Specia, L. (2018) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/5gudtrhqasz3 · Cited by 23
Using a randomised controlled clinical trial to evaluate an NLG system
Reiter, E., Robertson, R., Lennox, A. S., & Osman, L. M. (2001) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.3115/1073012.1073069 · Cited by 22
Evaluation as a service for information retrieval
Lin, J., & Efron, M. (2013) · ACM SIGIR Forum · doi:10.1145/2568388.2568390 · Cited by 21
Text specificity and impact on quality of news summaries
Louis, A., & Nenkova, A. (2012) · Cited by 20
Enabling accessibility through multimodality?
Himmelsbach, J., Garschall, M., Egger, S., Steffek, S., & Tscheligi, M. (2015) · International Conference on Mobile and Ubiquitous Multimedia (MUM) · doi:10.1145/2836041.2836060 · Cited by 17
An Evaluation of Procedural Instructional Text
Colineau, N., Paris, C. L., & Linden, K. V. (2002) · Cited by 15
What’s the Issue Here?: Task-based Evaluation of Reader Comment Summarization Systems
Barker, E., Paramita, M. L., Funk, A., Kurtić, E., Aker, A., Foster, J., Hepple, M., & Gaizauskas, R. (2016) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/2ruc7svh9yp8 · Cited by 9
Communication-based Evaluation for Natural Language GenerationPreprint
Newman, B., Cohn-Gordon, R., & Potts, C. (2019) · arXiv (Cornell University) · doi:10.7275/scil.1174 · Cited by 5
Distributed Container-Based Evaluation Platform for Private/Large Datasets
Eggel, I., Schaer, R., & Müller, H. (2018) · International Symposium on Parallel and Distributed Computing · doi:10.1109/ispdc2018.2018.00022 · Cited by 2
Flagged first, then by citations
Research works printed but not linked: 7
- Biswas Pradipta and Robinson Peter. 2010. A brief survey on user modelling in hci. In Proc. of the International Conference on Intelligent Human Computer Interaction (IHCI), volume 2010.
- Dudy Shiran and Bedrick Steven. 2020. Are some words worth more than others? In Proceedings of the First Workshop on Evaluation and Comparison of NLP Systems, pages 131– 142, Online. Association for Computational Linguistics.
- Krishnamurthy Balachander, Naryshkin Konstantin, and Wills Craig. 2011. Privacy leakage vs. protection measures: the growing disconnect. In Proceedings of the Web 2.0 Security and Privacy Workshop.
- Saracevic Tefko. 2006. Relevance: A Review of the Literature and a Framework for Thinking on the Notion in Information Science. Part II. Advances in Librarianship, 30: 3– 71.
- Saracevic Tefko. 2007. Relevance: A review of the literature and a framework for thinking on the notion in information science. Part III: Behavior and effects of relevance. Journal of the American Society for Information Science and Technology, 58(13): 2126– 2144.
- Scarton Carolina, Paetzold Gustavo, and Specia Lucia. 2018b. Text simplification from professionally produced corpora. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), Miyazaki, Japan. European Language Resources Association (ELRA).
- Wang William Yang, Mehdad Yashar, Radev Dragomir, and Stent Amanda. 2016. A low-rank approximation approach to learning joint embeddings of news stories and images for timeline summarization. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 58– 68.
Cited by
Sorted by citations
Draxler, F., Werner, A., Lehmann, F., Hoppe, M., Schmidt, A., Buschek, D., & Welsch, R. (2024) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3637875 · Cited by 186
LaMP: When Large Language Models Meet Personalization
Salemi, A., Mysore, S., Bendersky, M., & Zamani, H. (2023) · arXiv · doi:10.48550/arxiv.2304.11406 · Cited by 109
Kirk, H. R., Vidgen, B., Röttger, P., & Hale, S. A. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00820-y · Cited by 122
Optimization Methods for Personalizing Large Language Models through Retrieval Augmentation
Salemi, A., Kallumadi, S., & Zamani, H. (2024) · Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3626772.3657783 · Cited by 52
'It was 80% me, 20% AI': Seeking Authenticity in Co-Writing with Large Language Models
Hwang, A. H.-C., Liao, Q. V., Blodgett, S. L., Olteanu, A., & Trischler, A. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3711020 · Cited by 42
Strengths and Weaknesses of LLM-Based and Rule-Based NLP Technologies and Their Potential Synergies
Karanikolas, N. Ν., Manga, E., Samaridi, N. E., Stergiopoulos, V., Tousidou, E., & Vassilakopoulos, M. G. (2025) · Electronics · doi:10.3390/electronics14153064 · Cited by 24
Editable User Profiles for Controllable Text Recommendations
Mysore, S., Jasim, M., McCallum, A., & Zamani, H. (2023) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3539618.3591677 · Cited by 16
Leveraging Similar Users for Personalized Language Modeling with Limited Data
WELCH, C. A., Gu, C., Kummerfeld, J. K., Pérez‐Rosas, V., & Mihalcea, R. F. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.122 · Cited by 17
RECAP: Retrieval-Enhanced Context-Aware Prefix Encoder for Personalized Dialogue Response Generation
Liu, S., Cho, H., Freedman, M., Ma, X., & May, J. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.468 · Cited by 19
The Latest in Natural Language Generation: Trends, Tools and Applications in Industry
Skrodelis, H. K., Romānovs, A., Zeņina, N., & Gorskis, H. (2023) · IEEE Workshop on Advances in Information, Electronic and Electrical Engineering (AIEEE) · doi:10.1109/aieee58915.2023.10134841 · Cited by 10
Generating User-Engaging News Headlines
Cai, P., Song, K., Cho, S., Wang, H., Wang, X., Yu, H., Liu, F., & Dong, Y. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.183 · Cited by 12
A Survey on Conversational Search and Applications in Biomedicine
Adatrao, N. S. K., Gadireddy, G. R., & Noh, J. (2023) · ACM Southeast Regional Conference (ACMSE) · doi:10.1145/3564746.3587001 · Cited by 4
DecipherPref: Analyzing Influential Factors in Human Preference Judgments via GPT-4
Hu, Y., Song, K., Cho, S., Wang, X., Foroosh, H., & Liu, F. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.519 · Cited by 8
Steering AI-driven Personalization of Scientific Text for General Audiences
Kim, T., Agarwal, D., Ackerman, J., & Saha, M. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3757660 · Cited by 3
FedPerC: Federated Learning for Language Generation with Personal and Context Preference Embeddings
Silva, A., Tambwekar, P., & Gombolay, M. (2023) · Findings of the Association for Computational Linguistics: EACL · doi:10.18653/v1/2023.findings-eacl.64 · Cited by 2
A Major Obstacle for NLP Research: Let’s Talk about Time Allocation!
Kann, K., Dudy, S., & McCarthy, A. D. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.612 · Cited by 1
Self-citationPersonalization and Relevance in NLG
Dudy, S. (2022) · International World Wide Web Conference (WWW) · doi:10.1145/3487553.3527148
Self-citationBook Review: Reading Computer-Generated Texts
Zhang, T., & Wen, J. (2023) · New Media & Society · doi:10.1177/14614448221150773
Metadata
- Authors
- Dudy, S., Bedrick, S., & Webber, B.
- Affiliations
- University of Colorado Boulder (US)
- Oregon Health & Science University (US)
- University of Edinburgh (GB)
- Venue
- Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
- Published
- 2021
- Volume
- 2021
- Pages
- 5190-5202
- PMC
- PMC10328542
- OpenAlex
- W3200437427
- Cited by
- 17
- References
- 70
- Citations by year
- 2025: 3 · 2024: 2 · 2023: 9 · 2022: 3
- FWCI
- 1.61
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Funders
- National Science Foundation (2019805)
- National Institutes of Health
- National Institute on Deafness and Other Communication Disorders (R01DC015999)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- natural language generation, personalization, relevance, user intention, value-sensitive design
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.
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
