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Yixin Nie

Yixin Nie has 6 papers in our library, in 2018–2022, most of them on Reasoning, Dialogue systems, Information retrieval & search; often with Mohit Bansal, Xiang Zhou, Douwe Kiela.

6 papers in our library · 2018–2022

OpenAlex · ORCID · h-index 13 · 24 works · 1,735 citations OpenAlex, 2026-10-14

📄 6 works in our library · 🔗 Cited 958 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #2800

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🎓University of North Carolina at Chapel HillChapel Hill · United States2017–2022 · 17 papers🏢MetaMenlo Park · United States2021–2025 · 2 papers🎓Tsinghua UniversityBeijing · China2022Employment since 2022 · Doctoral candidate, Department of Automation

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 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 6 worksComputer Science · 6 worksArtificial Intelligence · 6 worksLanguage and Linguistics · 6 worksInformation Systems · 1 work

🧭 Topics 3Research subjects and languages the work is about.Reasoning · 4 worksInformation retrieval & search · 1 workDialogue systems · 1 work

 Works by year

2020s

20221 paperDistributed NLI: Learning to Predict Human Opinion Distributions for Language Reasoning20211 paperI like fish, especially dolphins: Addressing Contradictions in Dialogue Modeling20202 papersYixin Nie's papers of 2020

2010s

20191 paperRevealing the Importance of Semantic Retrieval for Machine Reading at Scale20181 paperAnalyzing Compositionality-Sensitivity of 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? (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 (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 (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 (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 (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 (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 · original ↗ · no summary yet · Proceedings of the 58th Annual Meeting of the Association for Computational…Yixin Nie et al.2020 · 1,272 citations
  2. What Can We Learn from Collective Human Opinions on Natural Language Inference Data?Yixin Nie, Xiang Zhou, Mohit BansalConference on Empirical Methods in Natural Language Processing (EMNLP) · 193 citations
  3. Revealing the Importance of Semantic Retrieval for Machine Reading at ScaleYixin Nie, Songhe Wang, Mohit BansalConference on Empirical Methods in Natural Language Processing (EMNLP) · 189 citations
  4. Analyzing Compositionality-Sensitivity of NLI ModelsYixin Nie, Yicheng Wang, Mohit BansalProceedings of the AAAI Conference on Artificial Intelligence · 119 citations
  5. I like fish, especially dolphins: Addressing Contradictions in Dialogue ModelingYixin Nie, Mary Williamson, Mohit Bansal, et al.Annual Meeting of the Association for Computational Linguistics (ACL) · 101 citations
  6. ConjNLI: Natural Language Inference Over Conjunctive SentencesSwarnadeep Saha, Yixin Nie, Mohit BansalConference on Empirical Methods in Natural Language Processing (EMNLP) · 50 citations
  7. Distributed NLI: Learning to Predict Human Opinion Distributions for Language ReasoningXiang Zhou, Yixin Nie, Mohit BansalFindings of the Association for Computational Linguistics · 35 citations

 Co-authors here

7 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 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 →.

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