--- title: "I like fish, especially dolphins — paper by Nie et al. (2021)" description: "The authors define DECODE (DialoguE COntradiction DEtection): given a dialogue, a model decides… 48 references, cited by 55 papers here." authors: ["Yixin Nie", "Mary Williamson", "Mohit Bansal", "Douwe Kiela", "Jason Weston"] year: 2021 venue: "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)" source: "https://aclanthology.org/2021.acl-long.134/" doi: "10.18653/v1/2021.acl-long.134" licence: "CC BY 4.0 (ACL Anthology)" licence_url: "https://creativecommons.org/licenses/by/4.0/" summary_licence: "Elamur Cosmic License" canonical: "https://research.elamur.ai/free/en/papers/nie-2021-i-like-fish-especially-dolphins/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/nie-2021-i-like-fish-especially-dolphins/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2021](https://md.research.elamur.ai/free/en/papers/2021/page/4/) / I like fish, especially dolphins: Addressing Contradictions in Dialogue Modeling # I like fish, especially dolphins: Addressing Contradictions in Dialogue Modeling **Authors:** [Yixin Nie](https://md.research.elamur.ai/free/en/authors/yixin-nie/), [Mary Williamson](https://md.research.elamur.ai/free/en/authors/mary-williamson/), [Mohit Bansal](https://md.research.elamur.ai/free/en/authors/mohit-bansal/), [Douwe Kiela](https://md.research.elamur.ai/free/en/authors/douwe-kiela/) and [Jason Weston](https://md.research.elamur.ai/free/en/authors/jason-weston/) · **Year:** 2021 · **Venue:** [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)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2021](https://md.research.elamur.ai/free/en/venues/acl/2021/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/) **Links:** [Publisher's page](https://aclanthology.org/2021.acl-long.134/) · [PDF](https://aclanthology.org/2021.acl-long.134.pdf) · [DOI 10.18653/v1/2021.acl-long.134](https://doi.org/10.18653/v1/2021.acl-long.134) · [OpenAlex](https://openalex.org/W3173803111) **Citations:** 46 citations (OpenAlex, 2026-10-14) · [55](https://md.research.elamur.ai/free/en/papers/nie-2021-i-like-fish-especially-dolphins/cited-by/) in our library, of the 141,840 papers whose bibliographies we read ## Summary Open-domain chatbots often contradict themselves, and there has been no reliable way to measure how well models detect such inconsistencies. The authors define DECODE (DialoguE COntradiction DEtection): given a dialogue, a model decides whether the final utterance conflicts with earlier ones and ideally identifies which earlier utterances are involved. They built a dataset by having crowdworkers on Amazon Mechanical Turk (via ParlAI) extend existing dialogues with a deliberately contradictory turn. They also collected an out-of-distribution set of real human-bot chats in which humans labeled bot self-contradictions. The authors compare two approaches. The unstructured approach feeds the whole dialogue into a Transformer NLI model. The structured approach scores utterances in pairs. Training on DECODE works much better than training on existing NLI resources, including dialogue NLI. The utterance-based approach is also more robust and transfers better to human-bot conversations. The best detector agrees well with human judgments, so it can serve as an automatic consistency metric. Used for re-ranking, it also reduces contradictions in BlenderBot outputs. ## Key points - **Dataset size and sources:** The dataset contains 17,713 human-written contradictory dialogues, of which 4,121 were checked by three extra annotators. Source dialogues come from Wizard of Wikipedia, EmpatheticDialogues, Blended Skill Talk and ConvAI2. Non-contradictory dialogues were added so that labels are balanced. - **Splits and diagnostic sets:** - Main train/dev/test splits have 27,184, 4,026 and 4,216 examples. Dev and test contain only verified examples. - Two diagnostic sets are derived from the test split. A2T (2,079 examples) inserts two extra turns and keeps the contradiction label. RCT (2,011 examples) removes the evidence turns, which yields non-contradictions. - **Best model:** An utterance-based RoBERTa fine-tuned on DECODE performed best. - **Correlation with humans:** Across bots, its average contradiction score correlates with human-identified contradiction rates at Pearson r = 0.81. - **Re-ranking BlenderBot (BST 2.7B):** - With top-k (k = 40) decoding, re-ranking cut human-judged contradictions from 69.7% to 39.5% (p = 0.023 with three verifiers). - The detector's own flag rate fell to 2.6%, though this figure is optimistic because the same detector was used for both re-ranking and evaluation. - Data and code are available at parl.ai/projects/contradiction. *Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.* 📚 Shelves (4): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 9,086 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 8,889 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 9,296 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 9,206 works 🧭 Topics (1): [🗨️ Dialogue systems](https://md.research.elamur.ai/free/en/topics/dialogue-systems/) · 401 works ## Cite Cite as: Yixin Nie, Mary Williamson, Mohit Bansal, Douwe Kiela and Jason Weston. 2021. I like fish, especially dolphins: Addressing Contradictions in Dialogue Modeling. 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). The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{nie2021i, title = {{I like fish, especially dolphins: Addressing Contradictions in Dialogue Modeling}}, author = {Yixin Nie, Mary Williamson, Mohit Bansal, Douwe Kiela and Jason Weston}, booktitle = {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)}, year = {2021}, doi = {10.18653/v1/2021.acl-long.134}, url = {https://doi.org/10.18653/v1/2021.acl-long.134} } ``` - [← previous Compression, Transduction, and Creation: A Unified Framework for Evaluating Natural Language Generation](https://md.research.elamur.ai/free/en/papers/deng-2021-compression-transduction-creation-unified-framework/) - [next → Superbizarre Is Not Superb: Derivational Morphology Improves BERT's Interpretation of Complex Words](https://md.research.elamur.ai/free/en/papers/hofmann-2021-superbizarre-derivational/) - [↑ back to papers of 2021](https://md.research.elamur.ai/free/en/papers/2021/page/4/) ## References 48 references, 21 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. Daniel Adiwardana, Minh-Thang Luong, David R So, Jamie Hall, Noah Fiedel, Romal Thoppilan, Zi Yang, Apoorv Kulshreshtha, Gaurav Nemade, Yifeng Lu, et al. 2020. [Towards a human-like open-domain chatbot](https://md.research.elamur.ai/free/en/papers/adiwardana-2020-towards-human-like-open-domain/). arXiv preprint arXiv:2001.09977. 2. Jeesoo Bang, Hyungjong Noh, Yonghee Kim, and Gary Geunbae Lee. 2015. Example-based chat-oriented dialogue system with personalized long-term memory. In 2015 International Conference on Big Data and Smart Computing (BigComp), pages 238–243. IEEE. [find](https://scholar.google.com/scholar?q=Jeesoo+Bang%2C+Hyungjong+Noh%2C+Yonghee+Kim%2C+and+Gary+Geunbae+Lee%2E+2015%2E+Example%2Dbased+chat%2Doriented+dialogue+system+with+personalized+long%2Dterm+memory%2E+In+2015+International+Conference+on+Big+Data+and+Sm) 3. Jason Baumgartner, Savvas Zannettou, Brian Keegan, Megan Squire, and Jeremy Blackburn. 2020. The pushshift reddit dataset. In Proceedings of the International AAAI Conference on Web and Social Media, volume 14, pages 830–839. [find](https://scholar.google.com/scholar?q=Jason+Baumgartner%2C+Savvas+Zannettou%2C+Brian+Keegan%2C+Megan+Squire%2C+and+Jeremy+Blackburn%2E+2020%2E+The+pushshift+reddit+dataset%2E+In+Proceedings+of+the+International+AAAI+Conference+on+Web+and+Social+Media%2C+) 4. Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015. A large annotated corpus for learning natural language inference. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 632–642, Lisbon, Portugal. Association for Computati… [find](https://scholar.google.com/scholar?q=Samuel+R%2E+Bowman%2C+Gabor+Angeli%2C+Christopher+Potts%2C+and+Christopher+D%2E+Manning%2E+2015%2E+A+large+annotated+corpus+for+learning+natural+language+inference%2E+In+Proceedings+of+the+2015+Conference+on+Empirica) 5. Andrew P Bradley. 1997. The use of the area under the roc curve in the evaluation of machine learning algorithms. Pattern recognition, 30(7):1145–1159. [find](https://scholar.google.com/scholar?q=Andrew+P+Bradley%2E+1997%2E+The+use+of+the+area+under+the+roc+curve+in+the+evaluation+of+machine+learning+algorithms%2E+Pattern+recognition%2C+30%287%29%3A1145%E2%80%931159%2E) 6. [Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter,…](https://md.research.elamur.ai/free/en/papers/brown-2020-language-models-few-shot-learners/) 7. Kevin Clark, Minh-Thang Luong, Quoc V. Le, and Christopher D. Manning. 2020. ELECTRA: pre-training text encoders as discriminators rather than generators. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020. OpenReview.net. [find](https://scholar.google.com/scholar?q=Kevin+Clark%2C+Minh%2DThang+Luong%2C+Quoc+V%2E+Le%2C+and+Christopher+D%2E+Manning%2E+2020%2E+ELECTRA%3A+pre%2Dtraining+text+encoders+as+discriminators+rather+than+generators%2E+In+8th+International+Conference+on+Learning+R) 8. Ido Dagan, Oren Glickman, and Bernardo Magnini. 2005. The pascal recognising textual entailment challenge. In Machine Learning Challenges Workshop, pages 177–190. Springer. [find](https://scholar.google.com/scholar?q=Ido+Dagan%2C+Oren+Glickman%2C+and+Bernardo+Magnini%2E+2005%2E+The+pascal+recognising+textual+entailment+challenge%2E+In+Machine+Learning+Challenges+Workshop%2C+pages+177%E2%80%93190%2E+Springer%2E) 9. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. [BERT: Pre-training of deep bidirectional transformers for language understanding](https://md.research.elamur.ai/free/en/papers/devlin-2019-bert-pre-training-deep-bidirectional/). In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies,… 10. Emily Dinan, Varvara Logacheva, Valentin Malykh, Alexander Miller, Kurt Shuster, Jack Urbanek, Douwe Kiela, Arthur Szlam, Iulian Serban, Ryan Lowe, et al. 2020. [The second conversational intelligence challenge (convai2)](https://md.research.elamur.ai/free/en/papers/dinan-2019-second-conversational-intelligence-challenge-convai2/). In The NeurIPS’18 Competition, pages 187–208. Springer. 11. Emily Dinan, Stephen Roller, Kurt Shuster, Angela Fan, Michael Auli, and Jason Weston. 2019. Wizard of wikipedia: Knowledge-powered conversational agents. In 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019. OpenReview.net. [find](https://scholar.google.com/scholar?q=Emily+Dinan%2C+Stephen+Roller%2C+Kurt+Shuster%2C+Angela+Fan%2C+Michael+Auli%2C+and+Jason+Weston%2E+2019%2E+Wizard+of+wikipedia%3A+Knowledge%2Dpowered+conversational+agents%2E+In+7th+International+Conference+on+Learning+R) 12. Nouha Dziri, Ehsan Kamalloo, Kory Mathewson, and Osmar Zaiane. 2019a. Augmenting neural response generation with context-aware topical attention. In Proceedings of the First Workshop on NLP for Conversational AI, pages 18–31, Florence, Italy. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Nouha+Dziri%2C+Ehsan+Kamalloo%2C+Kory+Mathewson%2C+and+Osmar+Zaiane%2E+2019a%2E+Augmenting+neural+response+generation+with+context%2Daware+topical+attention%2E+In+Proceedings+of+the+First+Workshop+on+NLP+for+Conver) 13. Nouha Dziri, Ehsan Kamalloo, Kory Mathewson, and Osmar Zaiane. 2019b. Evaluating coherence in dialogue systems using entailment. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short… [find](https://scholar.google.com/scholar?q=Nouha+Dziri%2C+Ehsan+Kamalloo%2C+Kory+Mathewson%2C+and+Osmar+Zaiane%2E+2019b%2E+Evaluating+coherence+in+dialogue+systems+using+entailment%2E+In+Proceedings+of+the+2019+Conference+of+the+North+American+Chapter+of+) 14. Angela Fan, Mike Lewis, and Yann Dauphin. 2018. Hierarchical neural story generation. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 889–898, Melbourne, Australia. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Angela+Fan%2C+Mike+Lewis%2C+and+Yann+Dauphin%2E+2018%2E+Hierarchical+neural+story+generation%2E+In+Proceedings+of+the+56th+Annual+Meeting+of+the+Association+for+Computational+Linguistics+%28Volume+1%3A+Long+Papers%29) 15. Margaret Li, Stephen Roller, Ilia Kulikov, Sean Welleck, Y-Lan Boureau, Kyunghyun Cho, and Jason Weston. 2020. [Don’t say that!](https://md.research.elamur.ai/free/en/papers/li-2020-don-t-say-that-making/) making inconsistent dialogue unlikely with unlikelihood training. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 4715– 472… 16. Margaret Li, Jason Weston, and Stephen Roller. 2019. Acute-eval: Improved dialogue evaluation with optimized questions and multi-turn comparisons. arXiv preprint arXiv:1909.03087. — [arXiv:1909.03087](https://arxiv.org/abs/1909.03087) [find](https://scholar.google.com/scholar?q=Margaret+Li%2C+Jason+Weston%2C+and+Stephen+Roller%2E+2019%2E+Acute%2Deval%3A+Improved+dialogue+evaluation+with+optimized+questions+and+multi%2Dturn+comparisons%2E+arXiv+preprint+arXiv%3A1909%2E03087%2E+%E2%80%94+%5BarXiv%3A1909%2E03087%5D) 17. [Matt Gardner, Yoav Artzi, Victoria Basmov, Jonathan Berant, Ben Bogin, Sihao Chen, Pradeep Dasigi, Dheeru Dua, Yanai Elazar, Ananth Gottumukkala, Nitish Gupta, Hannaneh Hajishirzi, Gabriel Ilharco, Daniel Khashabi, Kevin Lin, Jiangming Liu, Nelson F. Liu, Phoebe Mulcaire, Qiang Ning, Sameer Singh, N…](https://md.research.elamur.ai/free/en/papers/gardner-2020-evaluating-models-local-decision-boundaries/) 18. Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692. [find](https://scholar.google.com/scholar?q=Yinhan+Liu%2C+Myle+Ott%2C+Naman+Goyal%2C+Jingfei+Du%2C+Mandar+Joshi%2C+Danqi+Chen%2C+Omer+Levy%2C+Mike+Lewis%2C+Luke+Zettlemoyer%2C+and+Veselin+Stoyanov%2E+2019%2E+Roberta%3A+A+robustly+optimized+bert+pretraining+approach%2E+a) 19. Mor Geva, Yoav Goldberg, and Jonathan Berant. 2019. Are we modeling the task or the annotator? an investigation of annotator bias in natural language understanding datasets. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conf… [find](https://scholar.google.com/scholar?q=Mor+Geva%2C+Yoav+Goldberg%2C+and+Jonathan+Berant%2E+2019%2E+Are+we+modeling+the+task+or+the+annotator%3F+an+investigation+of+annotator+bias+in+natural+language+understanding+datasets%2E+In+Proceedings+of+the+2019) 20. Gary Marcus. 2018. Deep learning: A critical appraisal. arXiv preprint arXiv:1801.00631. [find](https://scholar.google.com/scholar?q=Gary+Marcus%2E+2018%2E+Deep+learning%3A+A+critical+appraisal%2E+arXiv+preprint+arXiv%3A1801%2E00631%2E) 21. Herbert P Grice. 1975. Logic and conversation. In Speech acts, pages 41–58. Brill. [find](https://scholar.google.com/scholar?q=Herbert+P+Grice%2E+1975%2E+Logic+and+conversation%2E+In+Speech+acts%2C+pages+41%E2%80%9358%2E+Brill%2E) 22. Samuel Humeau, Kurt Shuster, Marie-Anne Lachaux, and Jason Weston. 2019. Poly-encoders: Transformer architectures and pre-training strategies for fast and accurate multi-sentence scoring. arXiv preprint arXiv:1905.01969. [find](https://scholar.google.com/scholar?q=Samuel+Humeau%2C+Kurt+Shuster%2C+Marie%2DAnne+Lachaux%2C+and+Jason+Weston%2E+2019%2E+Poly%2Dencoders%3A+Transformer+architectures+and+pre%2Dtraining+strategies+for+fast+and+accurate+multi%2Dsentence+scoring%2E+arXiv+prepri) 23. Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2020. [BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension](https://md.research.elamur.ai/free/en/papers/lewis-2020-bart-denoising-sequence-sequence-pre/). In Proceedings of the 58th Annual Meeting of the… 24. Jiwei Li, Michel Galley, Chris Brockett, Georgios Spithourakis, Jianfeng Gao, and Bill Dolan. 2016. [A persona-based neural conversation model](https://md.research.elamur.ai/free/en/papers/li-2016-persona-based-neural-conversation-model/). In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 994–1003, Berlin, Germany. Associat… 25. Ryan Lowe, Nissan Pow, Iulian Serban, and Joelle Pineau. 2015. [The Ubuntu dialogue corpus: A large dataset for research in unstructured multi-turn dialogue systems](https://md.research.elamur.ai/free/en/papers/lowe-2015-ubuntu-dialogue-corpus-large-dataset/). In Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 285–294, Prague, Czech Republi… 26. Alexander Miller, Will Feng, Dhruv Batra, Antoine Bordes, Adam Fisch, Jiasen Lu, Devi Parikh, and Jason Weston. 2017. [ParlAI: A dialog research software platform](https://md.research.elamur.ai/free/en/papers/miller-2017-parlai-dialog-research-software-platform/). In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 79–84, Copenhagen… 27. Clifford Nass and Youngme Moon. 2000. Machines and mindlessness: Social responses to computers. Journal of social issues, 56(1):81–103. [find](https://scholar.google.com/scholar?q=Clifford+Nass+and+Youngme+Moon%2E+2000%2E+Machines+and+mindlessness%3A+Social+responses+to+computers%2E+Journal+of+social+issues%2C+56%281%29%3A81%E2%80%93103%2E) 28. Yixin Nie, Adina Williams, Emily Dinan, Mohit Bansal, Jason Weston, and Douwe Kiela. 2020a. Adversarial NLI: A new benchmark for natural language understanding. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 4885–4901, Online. Association for Comput… [find](https://scholar.google.com/scholar?q=Yixin+Nie%2C+Adina+Williams%2C+Emily+Dinan%2C+Mohit+Bansal%2C+Jason+Weston%2C+and+Douwe+Kiela%2E+2020a%2E+Adversarial+NLI%3A+A+new+benchmark+for+natural+language+understanding%2E+In+Proceedings+of+the+58th+Annual+Meeti) 29. Yixin Nie, Xiang Zhou, and Mohit Bansal. 2020b. [What can we learn from collective human opinions on natural language inference data?](https://md.research.elamur.ai/free/en/papers/nie-2020-what-can-we-learn-collective/) In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 9131–9143, Online. Association for Computational Linguistics. 30. Qiao Qian, Minlie Huang, Haizhou Zhao, Jingfang Xu, and Xiaoyan Zhu. 2018. Assigning personality/profile to a chatting machine for coherent conversation generation. In Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI 2018, July 13-19, 2018, Stockholm… [find](https://scholar.google.com/scholar?q=Qiao+Qian%2C+Minlie+Huang%2C+Haizhou+Zhao%2C+Jingfang+Xu%2C+and+Xiaoyan+Zhu%2E+2018%2E+Assigning+personality%2Fprofile+to+a+chatting+machine+for+coherent+conversation+generation%2E+In+Proceedings+of+the+Twenty%2DSevent) 31. Hannah Rashkin, Eric Michael Smith, Margaret Li, and Y-Lan Boureau. 2019. Towards empathetic open-domain conversation models: A new benchmark and dataset. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5370–5381, Florence, Italy. Association for Com… [find](https://scholar.google.com/scholar?q=Hannah+Rashkin%2C+Eric+Michael+Smith%2C+Margaret+Li%2C+and+Y%2DLan+Boureau%2E+2019%2E+Towards+empathetic+open%2Ddomain+conversation+models%3A+A+new+benchmark+and+dataset%2E+In+Proceedings+of+the+57th+Annual+Meeting+of+) 32. Marco Tulio Ribeiro, Tongshuang Wu, Carlos Guestrin, and Sameer Singh. 2020. Beyond accuracy: Behavioral testing of NLP models with CheckList. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 4902– 4912, Online. Association for Computational Linguisti… [find](https://scholar.google.com/scholar?q=Marco+Tulio+Ribeiro%2C+Tongshuang+Wu%2C+Carlos+Guestrin%2C+and+Sameer+Singh%2E+2020%2E+Beyond+accuracy%3A+Behavioral+testing+of+NLP+models+with+CheckList%2E+In+Proceedings+of+the+58th+Annual+Meeting+of+the+Associat) 33. Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M Smith, et al. 2020. [Recipes for building an open-domain chatbot](https://md.research.elamur.ai/free/en/papers/roller-2021-recipes-building-open-domain-chatbot/). arXiv preprint arXiv:2004.13637. 34. Abigail See, Stephen Roller, Douwe Kiela, and Jason Weston. 2019. [What makes a good conversation? how controllable attributes affect human judgments](https://md.research.elamur.ai/free/en/papers/see-2019-what-makes-good-conversation-how/). In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Vol… 35. Di Wang, Nebojsa Jojic, Chris Brockett, and Eric Nyberg. 2017. Steering output style and topic in neural response generation. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2140–2150, Copenhagen, Denmark. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Di+Wang%2C+Nebojsa+Jojic%2C+Chris+Brockett%2C+and+Eric+Nyberg%2E+2017%2E+Steering+output+style+and+topic+in+neural+response+generation%2E+In+Proceedings+of+the+2017+Conference+on+Empirical+Methods+in+Natural+Lang) 36. Sean Welleck, Jason Weston, Arthur Szlam, and Kyunghyun Cho. 2019. [Dialogue natural language inference](https://md.research.elamur.ai/free/en/papers/welleck-2019-dialogue-natural-language-inference/). In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 3731–3741, Florence, Italy. Association for Computational Linguistics. 37. Jason Weston, Emily Dinan, and Alexander Miller. 2018. [Retrieve and refine: Improved sequence generation models for dialogue](https://md.research.elamur.ai/free/en/papers/weston-2018-retrieve-refine-improved-sequence-generation/). In Proceedings of the 2018 EMNLP Workshop SCAI: The 2nd International Workshop on Search-Oriented Conversational AI, pages 87–92, Brussels, Belgium. Association for Computati… 38. Adina Williams, Nikita Nangia, and Samuel Bowman. 2018. A broad-coverage challenge corpus for sentence understanding through inference. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Pa… [find](https://scholar.google.com/scholar?q=Adina+Williams%2C+Nikita+Nangia%2C+and+Samuel+Bowman%2E+2018%2E+A+broad%2Dcoverage+challenge+corpus+for+sentence+understanding+through+inference%2E+In+Proceedings+of+the+2018+Conference+of+the+North+American+Chap) 39. S Worsnick. 2018. Mitsuku wins loebner prize 2018. [find](https://scholar.google.com/scholar?q=S+Worsnick%2E+2018%2E+Mitsuku+wins+loebner+prize+2018%2E) 40. Iulian Vlad Serban, Alessandro Sordoni, Yoshua Bengio, Aaron C. Courville, and Joelle Pineau. 2016. [Building end-to-end dialogue systems using generative hierarchical neural network models](https://md.research.elamur.ai/free/en/papers/serban-2015-building-end-end-dialogue-systems/). In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, February 12-17, 2016, Phoenix, Ari… 41. Iulian Vlad Serban, Alessandro Sordoni, Ryan Lowe, Laurent Charlin, Joelle Pineau, Aaron C. Courville, and Yoshua Bengio. 2017. [A hierarchical latent variable encoder-decoder model for generating dialogues](https://md.research.elamur.ai/free/en/papers/serban-2016-hierarchical-latent-variable-encoder-decoder/). In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, February 4-9,… 42. Eric Michael Smith, Mary Williamson, Kurt Shuster, Jason Weston, and Y-Lan Boureau. 2020. [Can you put it all together: Evaluating conversational agents’ ability to blend skills](https://md.research.elamur.ai/free/en/papers/smith-2020-can-you-put-it-all/). In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 2021–2030, Online. Assoc… 43. Ashwin K Vijayakumar, Michael Cogswell, Ramprasath R Selvaraju, Qing Sun, Stefan Lee, David Crandall, and Dhruv Batra. 2016. [Diverse beam search: Decoding diverse solutions from neural sequence models](https://md.research.elamur.ai/free/en/papers/vijayakumar-2016-diverse-beam-search-decoding-diverse/). arXiv preprint arXiv:1610.02424. 44. Saizheng Zhang, Emily Dinan, Jack Urbanek, Arthur Szlam, Douwe Kiela, and Jason Weston. 2018. [Personalizing dialogue agents: I have a dog, do you have pets too?](https://md.research.elamur.ai/free/en/papers/zhang-2018-personalizing-dialogue-agents-i-have/) In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2204– 2213, Melbo… 45. Wei-Nan Zhang, Qingfu Zhu, Yifa Wang, Yanyan Zhao, and Ting Liu. 2019a. Neural personalized response generation as domain adaptation. World Wide Web, 22(4):1427–1446. [find](https://scholar.google.com/scholar?q=Wei%2DNan+Zhang%2C+Qingfu+Zhu%2C+Yifa+Wang%2C+Yanyan+Zhao%2C+and+Ting+Liu%2E+2019a%2E+Neural+personalized+response+generation+as+domain+adaptation%2E+World+Wide+Web%2C+22%284%29%3A1427%E2%80%931446%2E) 46. Yizhe Zhang, Xiang Gao, Sungjin Lee, Chris Brockett, Michel Galley, Jianfeng Gao, and Bill Dolan. 2019b. Consistent dialogue generation with self-supervised feature learning. arXiv preprint arXiv:1903.05759. — [arXiv:1903.05759](https://arxiv.org/abs/1903.05759) [find](https://scholar.google.com/scholar?q=Yizhe+Zhang%2C+Xiang+Gao%2C+Sungjin+Lee%2C+Chris+Brockett%2C+Michel+Galley%2C+Jianfeng+Gao%2C+and+Bill+Dolan%2E+2019b%2E+Consistent+dialogue+generation+with+self%2Dsupervised+feature+learning%2E+arXiv+preprint+arXiv%3A1903) 47. Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, and Bill Dolan. 2020. [DIALOGPT : Large-scale generative pre-training for conversational response generation](https://md.research.elamur.ai/free/en/papers/zhang-2019-dialogpt-large-scale-generative-pre/). In Proceedings of the 58th Annual Meeting of the Association for Computational Ling… 48. Li Zhou, Jianfeng Gao, Di Li, and Heung-Yeung Shum. 2020. The design and implementation of XiaoIce, an empathetic social chatbot. Computational Linguistics, 46(1):53–93. [find](https://scholar.google.com/scholar?q=Li+Zhou%2C+Jianfeng+Gao%2C+Di+Li%2C+and+Heung%2DYeung+Shum%2E+2020%2E+The+design+and+implementation+of+XiaoIce%2C+an+empathetic+social+chatbot%2E+Computational+Linguistics%2C+46%281%29%3A53%E2%80%9393%2E) *Updated 24 October 2026*