--- title: "Yixin Nie — author profile and papers — Elamur Research" description: "Yixin Nie: 6 papers on Elamur Research (2018–2022), h-index 13 — affiliations, topics, co-authors and who cites them in our library." canonical: "https://research.elamur.ai/free/en/authors/yixin-nie/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/yixin-nie/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Yixin Nie # Yixin Nie Yixin Nie has 6 papers in our library, in 2018–2022, most of them on [Reasoning](https://md.research.elamur.ai/free/en/topics/reasoning/), [Dialogue systems](https://md.research.elamur.ai/free/en/topics/dialogue-systems/), [Information retrieval & search](https://md.research.elamur.ai/free/en/topics/information-retrieval/); often with [Mohit Bansal](https://md.research.elamur.ai/free/en/authors/mohit-bansal/), [Xiang Zhou](https://md.research.elamur.ai/free/en/authors/xiang-zhou/), [Douwe Kiela](https://md.research.elamur.ai/free/en/authors/douwe-kiela/). 6 papers in our library · 2018–2022 [OpenAlex](https://openalex.org/A5066141653) · [ORCID](https://orcid.org/0009-0007-2689-7441) · [**h-index 13**](https://openalex.org/authors/A5066141653) · [24 works](https://openalex.org/works?filter=author.id:A5066141653) · [1,735 citations](https://openalex.org/works?filter=author.id:A5066141653&sort=cited_by_count:desc) OpenAlex, 2026-10-14 [📄 6 works in our library](https://md.research.elamur.ai/free/en/authors/yixin-nie/#papers) · [🔗 Cited 958 times in our library](https://md.research.elamur.ai/free/en/authors/yixin-nie/cited-by/) · [1 more in our library without a page yet](#also) · [⭐ Paper Stars ranking: #2800](https://md.research.elamur.ai/free/en/paper-stars/page/28/#star-2800) The profile is the library's match of the name; two people with one name are not always told apart. Elsewhere: [Google Scholar](https://scholar.google.com/citations?view_op=search_authors&mauthors=Yixin+Nie) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Yixin+Nie) · [dblp](https://dblp.org/search?q=Yixin+Nie) · [ACL Anthology](https://aclanthology.org/search/?q=Yixin+Nie) ## Affiliations [🎓**University of North Carolina at Chapel Hill** Chapel Hill · United States 2017–2022 · 17 papers](https://md.research.elamur.ai/free/en/universities/university-of-north-carolina-at-chapel-hill/) · [🏢**Meta** Menlo Park · United States 2021–2025 · 2 papers](https://md.research.elamur.ai/free/en/companies/meta/) · [🎓**Tsinghua University** Beijing · China 2022Employment since 2022 · Doctoral candidate, Department of Automation](https://md.research.elamur.ai/free/en/universities/tsinghua-university/) From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed. ## Concepts, shelves and topics Of its works in our library: the number of works on each and its rating among them. 📚 Shelves 5 The field of science this work belongs to (OpenAlex fields).[Arts and Humanities · 6 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · [Computer Science · 6 works](https://md.research.elamur.ai/free/en/shelves/computer-science/) · [Artificial Intelligence · 6 works](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · [Language and Linguistics · 6 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · [Information Systems · 1 work](https://md.research.elamur.ai/free/en/shelves/computer-science/information-systems/) 🧭 Topics 3 Research subjects and languages the work is about.[Reasoning · 4 works](https://md.research.elamur.ai/free/en/topics/reasoning/) · [Information retrieval & search · 1 work](https://md.research.elamur.ai/free/en/topics/information-retrieval/) · [Dialogue systems · 1 work](https://md.research.elamur.ai/free/en/topics/dialogue-systems/) ## Works by year ### 2020s [**2022** 1 paper Distributed NLI: Learning to Predict Human Opinion Distributions for Language Reasoning](https://md.research.elamur.ai/free/en/papers/zhou-2022-distributed-nli-learning-predict-human/) · [**2021** 1 paper I like fish, especially dolphins: Addressing Contradictions in Dialogue Modeling](https://md.research.elamur.ai/free/en/papers/nie-2021-i-like-fish-especially-dolphins/) · [**2020** 2 papers Yixin Nie's papers of 2020](https://md.research.elamur.ai/free/en/authors/yixin-nie/year/2020/) ### 2010s [**2019** 1 paper Revealing the Importance of Semantic Retrieval for Machine Reading at Scale](https://md.research.elamur.ai/free/en/papers/nie-2019-revealing-importance-semantic-retrieval-machine/) · [**2018** 1 paper Analyzing Compositionality-Sensitivity of NLI Models](https://md.research.elamur.ai/free/en/papers/nie-2018-analyzing-compositionality-sensitivity-nli-models/) ## Papers The most cited first (OpenAlex and our library). 1. **[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/)** *(2020 · 193 citations)* NLU benchmarks usually treat the majority label as the only correct answer. This ignores the fact that people often genuinely disagree on inference judgments. The authors examine what is lost by this simplification. They built ChaosNLI, which gathers 100 crowdsourced labels per item for existing English NLI… 2. **[Revealing the Importance of Semantic Retrieval for Machine Reading at Scale](https://md.research.elamur.ai/free/en/papers/nie-2019-revealing-importance-semantic-retrieval-machine/)** *(2019 · 189 citations)* Machine Reading at Scale (MRS) systems must find relevant evidence in a huge text collection and then reason over it. 3. **[Analyzing Compositionality-Sensitivity of NLI Models](https://md.research.elamur.ai/free/en/papers/nie-2018-analyzing-compositionality-sensitivity-nli-models/)** *(2018 · 119 citations)* NLI systems reach near-human accuracy on SNLI and MNLI. This paper asks whether they use sentence structure or mostly match words. The authors test seven strong models, both sentence encoders and co-attention models. They build two rule-based adversaries that keep the vocabulary of the premise but change its meaning.… 4. **[I like fish, especially dolphins: Addressing Contradictions in Dialogue Modeling](https://md.research.elamur.ai/free/en/papers/nie-2021-i-like-fish-especially-dolphins/)** *(2021 · 101 citations)* Open-domain chatbots often contradict themselves, and there has been no reliable way to measure how well models detect such inconsistencies. 5. **[ConjNLI: Natural Language Inference Over Conjunctive Sentences](https://md.research.elamur.ai/free/en/papers/saha-2020-conjnli-natural-language-inference-over/)** *(2020 · 50 citations)* ConjNLI is a stress-test for natural language inference (NLI) built around coordinating conjunctions ("and", "or", "but", "nor"). 6. **[Distributed NLI: Learning to Predict Human Opinion Distributions for Language Reasoning](https://md.research.elamur.ai/free/en/papers/zhou-2022-distributed-nli-learning-predict-human/)** *(2022 · 35 citations)* The paper treats NLI as predicting how a crowd splits its judgements, not as picking one correct label. The authors call this task distributed NLI. They build on earlier datasets with many annotations per example, chiefly ChaosNLI-S and ChaosNLI-M (drawn from SNLI and MNLI), and add an experimental protocol for… ## All works 7 works of theirs in our library, the most cited first; 6 with a page here, the others link their original until we summarise them. 1. **[Adversarial NLI: A New Benchmark for Natural Language Understanding](https://aclanthology.org/2020.acl-main.441/) Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · original ↗ · no summary yet**Yixin Nie et al.*2020 · 1,272 citations* 2. **[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/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yixin Nie, Xiang Zhou, Mohit Bansal*2020 · 193 citations* 3. **[Revealing the Importance of Semantic Retrieval for Machine Reading at Scale](https://md.research.elamur.ai/free/en/papers/nie-2019-revealing-importance-semantic-retrieval-machine/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yixin Nie, Songhe Wang, Mohit Bansal*2019 · 189 citations* 4. **[Analyzing Compositionality-Sensitivity of NLI Models](https://md.research.elamur.ai/free/en/papers/nie-2018-analyzing-compositionality-sensitivity-nli-models/) [Proceedings of the AAAI Conference on Artificial Intelligence](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-aaai-conference-on-artificial-intelligence/)**Yixin Nie, Yicheng Wang, Mohit Bansal*2018 · 119 citations* 5. **[I like fish, especially dolphins: Addressing Contradictions in Dialogue Modeling](https://md.research.elamur.ai/free/en/papers/nie-2021-i-like-fish-especially-dolphins/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yixin Nie, Mary Williamson, Mohit Bansal, et al.*2021 · 101 citations* 6. **[ConjNLI: Natural Language Inference Over Conjunctive Sentences](https://md.research.elamur.ai/free/en/papers/saha-2020-conjnli-natural-language-inference-over/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Swarnadeep Saha, Yixin Nie, Mohit Bansal*2020 · 50 citations* 7. **[Distributed NLI: Learning to Predict Human Opinion Distributions for Language Reasoning](https://md.research.elamur.ai/free/en/papers/zhou-2022-distributed-nli-learning-predict-human/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xiang Zhou, Yixin Nie, Mohit Bansal*2022 · 35 citations* ## Co-authors here 7 co-authors with a page, the most papers together first. - [Mohit Bansal](https://md.research.elamur.ai/free/en/authors/mohit-bansal/) ×6 - [Xiang Zhou](https://md.research.elamur.ai/free/en/authors/xiang-zhou/) ×2 - [Douwe Kiela](https://md.research.elamur.ai/free/en/authors/douwe-kiela/) - [Jason Weston](https://md.research.elamur.ai/free/en/authors/jason-weston/) - [Mary Williamson](https://md.research.elamur.ai/free/en/authors/mary-williamson/) - [Swarnadeep Saha](https://md.research.elamur.ai/free/en/authors/swarnadeep-saha/) - [Yicheng Wang](https://md.research.elamur.ai/free/en/authors/yicheng-wang/) ## Venues Where their works here were published, the most works first, with the editions of those that have a page. - [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/) ×3: [EMNLP 2020](https://md.research.elamur.ai/free/en/venues/emnlp/2020/) ×2, [EMNLP 2019](https://md.research.elamur.ai/free/en/venues/emnlp/2019/) - [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/) ×2: [ACL 2021](https://md.research.elamur.ai/free/en/venues/acl/2021/) - [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/): [Findings of the Association for Computational Linguistics 2022](https://md.research.elamur.ai/free/en/venues/findings/2022/) - [Proceedings of the AAAI Conference on Artificial Intelligence](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-aaai-conference-on-artificial-intelligence/): [Proceedings of the AAAI Conference on Artificial Intelligence 2018](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-aaai-conference-on-artificial-intelligence/2018/) ## Cited by Papers of our library cite their works 958 times (of the 141,840 papers whose bibliographies we read); [126 citing papers with a page here →](https://md.research.elamur.ai/free/en/authors/yixin-nie/cited-by/). Updated 24 October 2026