---
title: "Who cites Iulian Vlad Serban"
description: "1,248 papers of the Elamur Research library cite the works of Iulian Vlad Serban, found in their bibliographies · page 1 of 2 · 1–100; the most cited first."
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---
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# Who cites Iulian Vlad Serban

1,816 citations of their works from 1,248 papers of our library (of the 140,884 whose bibliographies we read) · 103 with a page here, the most cited first · page 1 of 2 · 1–100 · [← Iulian Vlad Serban](https://md.research.elamur.ai/free/en/authors/iulian-vlad-serban/)

**1** [2](https://md.research.elamur.ai/free/en/authors/iulian-vlad-serban/cited-by/page/2/) [next →](https://md.research.elamur.ai/free/en/authors/iulian-vlad-serban/cited-by/page/2/)

1. [**Survey of Hallucination in Natural Language Generation**Ziwei Ji, Nayeon Lee, Rita Frieske, et al.*2022 · 5,947 citations*](https://md.research.elamur.ai/free/en/papers/ji-2022-survey-hallucination-natural-language-generation/)
2. [**A Diversity-Promoting Objective Function for Neural Conversation Models**Jiwei Li, Michel Galley, Chris Brockett, et al.*2016 · 2,925 citations*](https://md.research.elamur.ai/free/en/papers/li-2016-diversity-promoting-objective-function-neural/)
3. [**SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems**Alex Wang, Yada Pruksachatkun, Nikita Nangia, et al.*2019 · 2,187 citations*](https://md.research.elamur.ai/free/en/papers/wang-2019-superglue-stickier-benchmark-general-purpose/)
4. [**Personalizing Dialogue Agents: I have a dog, do you have pets too?**Saizheng Zhang, Emily Dinan, Jack Urbanek, et al.*2018 · 1,839 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2018-personalizing-dialogue-agents-i-have/)
5. [**OPT: Open Pre-trained Transformer Language Models**Susan Zhang, Stephen Roller, Naman Goyal, et al.*2022 · 1,709 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2022-opt-open-pre-trained-transformer/)
6. [**MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations**Soujanya Poria, Devamanyu Hazarika, Navonil Majumder, et al.*2018 · 1,668 citations*](https://md.research.elamur.ai/free/en/papers/poria-2018-meld-multimodal-multi-party-dataset/)
7. [**A Deep Reinforced Model for Abstractive Summarization**Romain Paulus, Caiming Xiong, Richard Socher*2017 · 1,626 citations*](https://md.research.elamur.ai/free/en/papers/paulus-2017-deep-reinforced-model-abstractive-summarization/)
8. [**Deep Reinforcement Learning for Dialogue Generation**Jiwei Li, Will Monroe, Alan Ritter, et al.*2016 · 1,479 citations*](https://md.research.elamur.ai/free/en/papers/li-2016-deep-reinforcement-learning-dialogue-generation/)
9. [**LaMDA: Language Models for Dialog Applications**Romal Thoppilan, Daniel De Freitas, Jamie Hall, et al.*2022 · 1,314 citations*](https://md.research.elamur.ai/free/en/papers/thoppilan-2022-lamda-language-models-dialog-applications/)
10. [**A Comprehensive Overview of Large Language Models**Humza Naveed, Asad Ullah Khan, Shi Qiu, et al.*2023 · 1,286 citations*](https://md.research.elamur.ai/free/en/papers/naveed-2023-comprehensive-overview-large-language-models/)
11. [**Recipes for Building an Open-Domain Chatbot**Stephen Roller, Emily Dinan, Naman Goyal, et al.*2021 · 1,206 citations*](https://md.research.elamur.ai/free/en/papers/roller-2021-recipes-building-open-domain-chatbot/)
12. [**DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset**Yanran Li, Hui Su, Xiaoyu Shen, et al.*2017 · 1,195 citations*](https://md.research.elamur.ai/free/en/papers/li-2017-dailydialog-manually-labelled-multi-turn/)
13. [**Adversarial Learning for Neural Dialogue Generation**Jiwei Li, Will Monroe, Tianlin Shi, et al.*2017 · 1,044 citations*](https://md.research.elamur.ai/free/en/papers/li-2017-adversarial-learning-neural-dialogue-generation/)
14. [**Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders**Tiancheng Zhao, Ran Zhao, Maxine Eskenazi*2017 · 986 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2017-learning-discourse-level-diversity-neural/)
15. [**Challenges in Data-to-Document Generation**Sam Wiseman, Stuart M. Shieber, Alexander M. Rush*2017 · 846 citations*](https://md.research.elamur.ai/free/en/papers/wiseman-2017-challenges-data-document-generation/)
16. [**MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distance**Wei Zhao, Maxime Peyrard, Fei Liu, et al.*2019 · 822 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2019-moverscore-text-generation-evaluating-contextualized/)
17. [**Is ChatGPT a General-Purpose Natural Language Processing Task Solver?**Chengwei Qin, Aston Zhang, Zhuosheng Zhang, et al.*2023 · 818 citations*](https://md.research.elamur.ai/free/en/papers/qin-2023-chatgpt-general-purpose-natural-language/)
18. [**Scaling Neural Machine Translation**Myle Ott, Sergey Edunov, David Grangier, et al.*2018 · 813 citations*](https://md.research.elamur.ai/free/en/papers/ott-2018-scaling-neural-machine-translation/)
19. [**Can Large Language Models Transform Computational Social Science?**Caleb Ziems, William Held, Omar Shaikh, et al.*2023 · 776 citations*](https://md.research.elamur.ai/free/en/papers/ziems-2023-large-language-models-transform-computational/)
20. [**Learning Distributed Representations of Sentences from Unlabelled Data**Felix Hill, Kyunghyun Cho, Anna Korhonen*2016 · 756 citations*](https://md.research.elamur.ai/free/en/papers/hill-2016-learning-distributed-representations-sentences-unlabelled/)
21. [**DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation**Yizhe Zhang, Siqi Sun, Michel Galley, et al.*2019 · 684 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2019-dialogpt-large-scale-generative-pre/)
22. [**Social Bias Frames: Reasoning about Social and Power Implications of Language**Maarten Sap, Saadia Gabriel, Lianhui Qin, et al.*2020 · 680 citations*](https://md.research.elamur.ai/free/en/papers/sap-2020-social-bias-frames-reasoning-about/)
23. [**Red Teaming Language Models with Language Models**Ethan Perez, Saffron Huang, Francis Song, et al.*2022 · 665 citations*](https://md.research.elamur.ai/free/en/papers/perez-2022-red-teaming-language-models-language/)
24. [**Towards a Human-like Open-Domain Chatbot**Daniel Adiwardana, Minh-Thang Luong, David R. So, et al.*2020 · 573 citations*](https://md.research.elamur.ai/free/en/papers/adiwardana-2020-towards-human-like-open-domain/)
25. [**Plug and Play Language Models: A Simple Approach to Controlled Text Generation**Sumanth Dathathri, Andrea Madotto, Janice Lan, et al.*2019 · 562 citations*](https://md.research.elamur.ai/free/en/papers/dathathri-2019-plug-play-language-models-simple/)
26. [**The Second Conversational Intelligence Challenge (ConvAI2)**Emily Dinan, Varvara Logacheva, Valentin Malykh, et al.*2019 · 525 citations*](https://md.research.elamur.ai/free/en/papers/dinan-2019-second-conversational-intelligence-challenge-convai2/)
27. [**Lexically Constrained Decoding for Sequence Generation Using Grid Beam Search**Chris Hokamp, Qun Liu*2017 · 520 citations*](https://md.research.elamur.ai/free/en/papers/hokamp-2017-lexically-constrained-decoding-sequence-generation/)
28. [**A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues**Iulian Vlad Serban, Alessandro Sordoni, Ryan Lowe, et al.*2016 · 518 citations*](https://md.research.elamur.ai/free/en/papers/serban-2016-hierarchical-latent-variable-encoder-decoder/)
29. [**How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation**Chia-Wei Liu, Ryan Lowe, Iulian Serban, et al.*2016 · 500 citations*](https://md.research.elamur.ai/free/en/papers/liu-2016-how-not-evaluate-your-dialogue/)
30. [**Neural Text Generation with Unlikelihood Training**Sean Welleck, Ilia Kulikov, Stephen Roller, et al.*2019 · 498 citations*](https://md.research.elamur.ai/free/en/papers/welleck-2019-neural-text-generation-unlikelihood-training/)
31. [**Towards an Automatic Turing Test: Learning to Evaluate Dialogue Responses**Ryan Lowe, Michael Noseworthy, Iulian V. Serban, et al.*2017 · 486 citations*](https://md.research.elamur.ai/free/en/papers/lowe-2017-towards-automatic-turing-test-learning/)
32. [**A Persona-Based Neural Conversation Model**Jiwei Li, Michel Galley, Chris Brockett, et al.*2016 · 456 citations*](https://md.research.elamur.ai/free/en/papers/li-2016-persona-based-neural-conversation-model/)
33. [**Deep Keyphrase Generation**Rui Meng, Sanqiang Zhao, Shuguang Han, et al.*2017 · 453 citations*](https://md.research.elamur.ai/free/en/papers/meng-2017-deep-keyphrase-generation/)
34. [**Experience Grounds Language**Yonatan Bisk, Ari Holtzman, Jesse Thomason, et al.*2020 · 429 citations*](https://md.research.elamur.ai/free/en/papers/bisk-2020-experience-grounds-language/)
35. [**Deal or No Deal? End-to-End Learning of Negotiation Dialogues**Mike Lewis, Denis Yarats, Yann Dauphin, et al.*2017 · 418 citations*](https://md.research.elamur.ai/free/en/papers/lewis-2017-deal-or-no-deal-end/)
36. [**Multi-Turn Response Selection for Chatbots with Deep Attention Matching Network**Xiangyang Zhou, Lu Li, Daxiang Dong, et al.*2018 · 408 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2018-multi-turn-response-selection-chatbots/)
37. [**Automatic Detection of Generated Text is Easiest when Humans are Fooled**Daphne Ippolito, Daniel Duckworth, Chris Callison-Burch, et al.*2019 · 406 citations*](https://md.research.elamur.ai/free/en/papers/ippolito-2019-automatic-detection-generated-text-easiest/)
38. [**Mem2Seq: Effectively Incorporating Knowledge Bases into End-to-End Task-Oriented Dialog Systems**Andrea Madotto, Chien-Sheng Wu, Pascale Fung*2018 · 401 citations*](https://md.research.elamur.ai/free/en/papers/madotto-2018-mem2seq-effectively-incorporating-knowledge-bases/)
39. [**Towards Emotional Support Dialog Systems**Siyang Liu, Chujie Zheng, Orianna Demasi, et al.*2021 · 391 citations*](https://md.research.elamur.ai/free/en/papers/liu-2021-emotional-support-dialog-systems/)
40. [**Retrieval Augmentation Reduces Hallucination in Conversation**Kurt Shuster, Spencer Poff, Moya Chen, et al.*2021 · 382 citations*](https://md.research.elamur.ai/free/en/papers/shuster-2021-retrieval-augmentation-reduces-hallucination-conversation/)
41. [**What makes a good conversation? How controllable attributes affect human judgments**Abigail See, Stephen Roller, Douwe Kiela, et al.*2019 · 369 citations*](https://md.research.elamur.ai/free/en/papers/see-2019-what-makes-good-conversation-how/)
42. [**Exploring Neural Text Simplification Models**Sergiu Nisioi, Sanja Štajner, Simone Paolo Ponzetto, et al.*2017 · 353 citations*](https://md.research.elamur.ai/free/en/papers/nisioi-2017-exploring-neural-text-simplification-models/)
43. [**FEQA: A Question Answering Evaluation Framework for Faithfulness Assessment in Abstractive Summarization**Esin Durmus, He He, Mona Diab*2020 · 350 citations*](https://md.research.elamur.ai/free/en/papers/durmus-2020-feqa-question-answering-evaluation-framework/)
44. [**Reformulating Unsupervised Style Transfer as Paraphrase Generation**Kalpesh Krishna, John Wieting, Mohit Iyyer*2020 · 348 citations*](https://md.research.elamur.ai/free/en/papers/krishna-2020-reformulating-unsupervised-style-transfer-as/)
45. [**Controlling Output Length in Neural Encoder-Decoders**Yuta Kikuchi, Graham Neubig, Ryohei Sasano, et al.*2016 · 323 citations*](https://md.research.elamur.ai/free/en/papers/kikuchi-2016-controlling-output-length-neural-encoder/)
46. [**MoEL: Mixture of Empathetic Listeners**Zhaojiang Lin, Andrea Madotto, Jamin Shin, et al.*2019 · 301 citations*](https://md.research.elamur.ai/free/en/papers/lin-2019-moel-mixture-empathetic-listeners/)
47. [**Learning to Write with Cooperative Discriminators**Ari Holtzman, Jan Buys, Maxwell Forbes, et al.*2018 · 301 citations*](https://md.research.elamur.ai/free/en/papers/holtzman-2018-learning-write-cooperative-discriminators/)
48. [**Build it Break it Fix it for Dialogue Safety: Robustness from Adversarial Human Attack**Emily Dinan, Samuel Humeau, Bharath Chintagunta, et al.*2019 · 291 citations*](https://md.research.elamur.ai/free/en/papers/dinan-2019-build-it-break-it-fix/)
49. [**Exploiting Cross-Sentence Context for Neural Machine Translation**Longyue Wang, Zhaopeng Tu, Andy Way, et al.*2017 · 291 citations*](https://md.research.elamur.ai/free/en/papers/wang-2017-exploiting-cross-sentence-context-neural/)
50. [**Can You Put it All Together: Evaluating Conversational Agents' Ability to Blend Skills**Eric Michael Smith, Mary Williamson, Kurt Shuster, et al.*2020 · 288 citations*](https://md.research.elamur.ai/free/en/papers/smith-2020-can-you-put-it-all/)
51. [**Why We Need New Evaluation Metrics for NLG**Jekaterina Novikova, Ondřej Dušek, Amanda Cercas Curry, et al.*2017 · 287 citations*](https://md.research.elamur.ai/free/en/papers/novikova-2017-why-we-need-new-evaluation/)
52. [**USR: An Unsupervised and Reference Free Evaluation Metric for Dialog Generation**Shikib Mehri, Maxine Eskenazi*2020 · 283 citations*](https://md.research.elamur.ai/free/en/papers/mehri-2020-usr-unsupervised-reference-free-evaluation/)
53. [**MIME: MIMicking Emotions for Empathetic Response Generation**Navonil Majumder, Pengfei Hong, Shanshan Peng, et al.*2020 · 277 citations*](https://md.research.elamur.ai/free/en/papers/majumder-2020-mime-mimicking-emotions-empathetic-response/)
54. [**Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Tools**Varun Magesh, Faiz Surani, Matthew Dahl, et al.*2024 · 267 citations*](https://md.research.elamur.ai/free/en/papers/magesh-2024-hallucination-free-assessing-reliability-leading/)
55. [**Handling Divergent Reference Texts when Evaluating Table-to-Text Generation**Bhuwan Dhingra, Manaal Faruqui, Ankur Parikh, et al.*2019 · 266 citations*](https://md.research.elamur.ai/free/en/papers/dhingra-2019-handling-divergent-reference-texts-when/)
56. [**Retrieve and Refine: Improved Sequence Generation Models For Dialogue**Jason Weston, Emily Dinan, Alexander Miller*2018 · 254 citations*](https://md.research.elamur.ai/free/en/papers/weston-2018-retrieve-refine-improved-sequence-generation/)
57. [**Knowledge-Grounded Dialogue Generation with Pre-trained Language Models**Xueliang Zhao, Wei Wu, Can Xu, et al.*2020 · 244 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2020-knowledge-grounded-dialogue-generation-pre/)
58. [**Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset**Bill Byrne, Karthik Krishnamoorthi, Chinnadhurai Sankar, et al.*2019 · 244 citations*](https://md.research.elamur.ai/free/en/papers/byrne-2019-taskmaster-1-realistic-diverse-dialog/)
59. [**MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers**Krishna Pillutla, Swabha Swayamdipta, Rowan Zellers, et al.*2021 · 243 citations*](https://md.research.elamur.ai/free/en/papers/pillutla-2021-mauve-measuring-gap-between-neural/)
60. [**Towards Exploiting Background Knowledge for Building Conversation Systems**Nikita Moghe, Siddhartha Arora, Suman Banerjee, et al.*2018 · 238 citations*](https://md.research.elamur.ai/free/en/papers/moghe-2018-exploiting-background-knowledge-building-conversation/)
61. [**OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization**Srinivasan Iyer, Xi Victoria Lin, Ramakanth Pasunuru, et al.*2022 · 237 citations*](https://md.research.elamur.ai/free/en/papers/iyer-2022-opt-iml-scaling-language-model/)
62. [**LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset**Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, et al.*2023 · 235 citations*](https://md.research.elamur.ai/free/en/papers/zheng-2023-lmsys-chat-1m-large-scale/)
63. [**BlenderBot 3: a deployed conversational agent that continually learns to responsibly engage**Kurt Shuster, Jing Xu, Mojtaba Komeili, et al.*2022 · 234 citations*](https://md.research.elamur.ai/free/en/papers/shuster-2022-blenderbot-3-deployed-conversational-agent/)
64. [**The Importance of Modeling Social Factors of Language: Theory and Practice**Dirk Hovy, Diyi Yang*2021 · 234 citations*](https://md.research.elamur.ai/free/en/papers/hovy-2021-importance-modeling-social-factors-language/)
65. [**Unsupervised Evaluation of Interactive Dialog with DialoGPT**Shikib Mehri, Maxine Eskenazi*2020 · 223 citations*](https://md.research.elamur.ai/free/en/papers/mehri-2020-unsupervised-evaluation-interactive-dialog-dialogpt/)
66. [**Don't Say That! Making Inconsistent Dialogue Unlikely with Unlikelihood Training**Margaret Li, Stephen Roller, Ilia Kulikov, et al.*2020 · 214 citations*](https://md.research.elamur.ai/free/en/papers/li-2020-don-t-say-that-making/)
67. [**Answers Unite! Unsupervised Metrics for Reinforced Summarization Models**Thomas Scialom, Sylvain Lamprier, Benjamin Piwowarski, et al.*2019 · 210 citations*](https://md.research.elamur.ai/free/en/papers/scialom-2019-answers-unite-unsupervised-metrics-reinforced/)
68. [**LLM-Eval: Unified Multi-Dimensional Automatic Evaluation for Open-Domain Conversations with Large Language Models**Yen-Ting Lin, Yun-Nung Chen*2023 · 206 citations*](https://md.research.elamur.ai/free/en/papers/lin-2023-llm-eval-unified-multi-dimensional/)
69. [**CONAN - COunter NArratives through Nichesourcing: a Multilingual Dataset of Responses to Fight Online Hate Speech**Yi-Ling Chung, Elizaveta Kuzmenko, Serra Sinem Tekiroglu, et al.*2019 · 205 citations*](https://md.research.elamur.ai/free/en/papers/chung-2019-conan-counter-narratives-through-nichesourcing/)
70. [**A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasets**Md Tahmid Rahman Laskar, M Saiful Bari, Mizanur Rahman, et al.*2023 · 198 citations*](https://md.research.elamur.ai/free/en/papers/laskar-2023-systematic-study-comprehensive-evaluation-chatgpt/)
71. [**Q²: Evaluating Factual Consistency in Knowledge-Grounded Dialogues via Question Generation and Question Answering**Or Honovich, Leshem Choshen, Roee Aharoni, et al.*2021 · 195 citations*](https://md.research.elamur.ai/free/en/papers/honovich-2021-q-2-evaluating-factual-consistency/)
72. [**MuTual: A Dataset for Multi-Turn Dialogue Reasoning**Leyang Cui, Yu Wu, Shujie Liu, et al.*2020 · 189 citations*](https://md.research.elamur.ai/free/en/papers/cui-2020-mutual-dataset-multi-turn-dialogue/)
73. [**Grounded Conversation Generation as Guided Traverses in Commonsense Knowledge Graphs**Houyu Zhang, Zhenghao Liu, Chenyan Xiong, et al.*2020 · 181 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2020-grounded-conversation-generation-as-guided/)
74. [**Two Tales of Persona in LLMs: A Survey of Role-Playing and Personalization**Yu-Min Tseng, Yu-Chao Huang, Teng-Yun Hsiao, et al.*2024 · 177 citations*](https://md.research.elamur.ai/free/en/papers/tseng-2024-two-tales-persona-llms-survey/)
75. [**Evaluating Large Language Models: A Comprehensive Survey**Zishan Guo, Renren Jin, Chuang Liu, et al.*2023 · 177 citations*](https://md.research.elamur.ai/free/en/papers/guo-2023-evaluating-large-language-models-comprehensive/)
76. [**MediaSum: A Large-scale Media Interview Dataset for Dialogue Summarization**Chenguang Zhu, Yang Liu, Jie Mei, et al.*2021 · 176 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2021-mediasum-large-scale-media-interview/)
77. [**Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity**Cunxiang Wang, Xiaoze Liu, Yuanhao Yue, et al.*2023 · 169 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-survey-factuality-large-language-models/)
78. [**“I'm sorry to hear that”: Finding New Biases in Language Models with a Holistic Descriptor Dataset**Eric Michael Smith, Melissa Hall, Melanie Kambadur, et al.*2022 · 165 citations*](https://md.research.elamur.ai/free/en/papers/smith-2022-i-m-sorry-hear-that/)
79. [**Generation-Augmented Retrieval for Open-domain Question Answering**Yuning Mao, Pengcheng He, Xiaodong Liu, et al.*2020 · 150 citations*](https://md.research.elamur.ai/free/en/papers/mao-2020-generation-augmented-retrieval-open-domain/)
80. [**GLUCOSE: GeneraLized and COntextualized Story Explanations**Nasrin Mostafazadeh, Aditya Kalyanpur, Lori Moon, et al.*2020 · 144 citations*](https://md.research.elamur.ai/free/en/papers/mostafazadeh-2020-glucose-generalized-contextualized-story-explanations/)
81. [**From Persona to Personalization: A Survey on Role-Playing Language Agents**Jiangjie Chen, Xintao Wang, Rui Xu, et al.*2024 · 114 citations*](https://md.research.elamur.ai/free/en/papers/chen-2024-persona-personalization-survey-role-playing/)
82. [**Improving Implicit Discourse Relation Classification by Modeling Inter-dependencies of Discourse Units in a Paragraph**Zeyu Dai, Ruihong Huang*2018 · 95 citations*](https://md.research.elamur.ai/free/en/papers/dai-2018-improving-implicit-discourse-relation-classification/)
83. [**Exploring the Potential of ChatGPT on Sentence Level Relations: A Focus on Temporal, Causal, and Discourse Relations**Chunkit Chan, Cheng Jiayang, Weiqi Wang, et al.*2024 · 92 citations*](https://md.research.elamur.ai/free/en/papers/chan-2024-exploring-potential-chatgpt-sentence-level/)
84. [**Will I Sound Like Me? Improving Persona Consistency in Dialogues through Pragmatic Self-Consciousness**Hyunwoo Kim, Byeongchang Kim, Gunhee Kim*2020 · 82 citations*](https://md.research.elamur.ai/free/en/papers/kim-2020-will-i-sound-like-me/)
85. [**Language Modeling for Morphologically Rich Languages: Character-Aware Modeling for Word-Level Prediction**Daniela Gerz, Ivan Vulić, Edoardo Ponti, et al.*2018 · 82 citations*](https://md.research.elamur.ai/free/en/papers/gerz-2018-language-modeling-morphologically-rich-languages/)
86. [**Dialogue Act Sequence Labeling using Hierarchical encoder with CRF**Harshit Kumar, Arvind Agarwal, Riddhiman Dasgupta, et al.*2017 · 75 citations*](https://md.research.elamur.ai/free/en/papers/kumar-2017-dialogue-act-sequence-labeling-using/)
87. [**Coreference and Coherence in Neural Machine Translation: A Study Using Oracle Experiments**Dario Stojanovski, Alexander Fraser*2018 · 46 citations*](https://md.research.elamur.ai/free/en/papers/stojanovski-2018-coreference-coherence-neural-machine-translation/)
88. [**One Model to Learn Both: Zero Pronoun Prediction and Translation**Longyue Wang, Zhaopeng Tu, Xing Wang, et al.*2019 · 41 citations*](https://md.research.elamur.ai/free/en/papers/wang-2019-one-model-learn-both-zero/)
89. [**GRICE: A Grammar-based Dataset for Recovering Implicature and Conversational rEasoning**Zilong Zheng, Shuwen Qiu, Lifeng Fan, et al.*2021 · 36 citations*](https://md.research.elamur.ai/free/en/papers/zheng-2021-grice-grammar-based-dataset-recovering/)
90. [**Reflect, Not Reflex: Inference-Based Common Ground Improves Dialogue Response Quality**Pei Zhou, Hyundong Cho, Pegah Jandaghi, et al.*2022 · 34 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2022-reflect-not-reflex-inference-based/)
91. [**Findings of the WMT 2020 Shared Task on Chat Translation**M. Amin Farajian, António V. Lopes, André F. T. Martins, et al.*2020 · 29 citations*](https://md.research.elamur.ai/free/en/papers/farajian-2020-findings-wmt-2020-shared-task/)
92. [**Exemplification Modeling: Can You Give Me an Example, Please?**Edoardo Barba, Luigi Procopio, Caterina Lacerra*2021 · 23 citations*](https://md.research.elamur.ai/free/en/papers/barba-2021-exemplification-modeling/)
93. [**Modeling Speech Acts in Asynchronous Conversations: A Neural-CRF Approach**Shafiq Joty, Tasnim Mohiuddin*2018 · 20 citations*](https://md.research.elamur.ai/free/en/papers/joty-2018-modeling-speech-acts-asynchronous-conversations/)
94. [**Discourse Relation Prediction and Discourse Parsing in Dialogues with Minimal Supervision**Chuyuan Li, Chloé Braud, Maxime Amblard, et al.*2024 · 12 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-discourse-relation-prediction-discourse-parsing/)
95. [**Dialogue Discourse Parsing as Generation: A Sequence-to-Sequence LLM-based Approach**Chuyuan Li, Yuwei Yin, Giuseppe Carenini*2024 · 11 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-dialogue-discourse-parsing-as-generation/)
96. [**A Distance-Aware Multi-Task Framework for Conversational Discourse Parsing**Yaxin Fan, Peifeng Li, Fang Kong, et al.*2022 · 11 citations*](https://md.research.elamur.ai/free/en/papers/fan-2022-distance-aware-multi-task-framework/)
97. [**Disentangling Indirect Answers to Yes-No Questions in Real Conversations**Krishna Sanagavarapu, Jathin Singaraju, Anusha Kakileti, et al.*2022 · 11 citations*](https://md.research.elamur.ai/free/en/papers/sanagavarapu-2022-disentangling-indirect-answers-yes-no/)
98. [**Task-Aware Self-Supervised Framework for Dialogue Discourse Parsing**Wei Li, Luyao Zhu, Wei Shao, et al.*2023 · 10 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-task-aware-self-supervised-framework/)
99. [**Speaker-Aware Discourse Parsing on Multi-Party Dialogues**Nan Yu, Guohong Fu, Min Zhang*2022 · 10 citations*](https://md.research.elamur.ai/free/en/papers/yu-2022-speaker-aware-discourse-parsing-multi/)
100. [**Automatic Evaluate Dialogue Appropriateness by Using Dialogue Act**Bao Chen, Yuanjie Wang, Zeming Liu, et al.*2023 · 6 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-automatic-evaluate-dialogue-appropriateness-dialogue/)

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