---
title: "McGill University — works"
description: "30 works by researchers of McGill University in our library, the most cited first."
canonical: "https://research.elamur.ai/free/en/universities/mcgill-university/works/"
---
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# Works of McGill University

30 works · the most cited first · [← McGill University](https://md.research.elamur.ai/free/en/universities/mcgill-university/)

1. [**StereoSet: Measuring stereotypical bias in pretrained language models**Moin Nadeem, Anna Bethke, Siva Reddy*2021 · 1,131 citations*](https://md.research.elamur.ai/free/en/papers/nadeem-2021-stereoset-measuring-stereotypical-bias-pretrained/)
2. [**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/)
3. [**Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models**Iulian V. Serban, Alessandro Sordoni, Yoshua Bengio, et al.*2015 · 524 citations*](https://md.research.elamur.ai/free/en/papers/serban-2015-building-end-end-dialogue-systems/)
4. [**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/)
5. [**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/)
6. [**The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems**Ryan Lowe, Nissan Pow, Iulian Serban, et al.*2015 · 474 citations*](https://md.research.elamur.ai/free/en/papers/lowe-2015-ubuntu-dialogue-corpus-large-dataset/)
7. [**TextWorld: A Learning Environment for Text-based Games**Marc-Alexandre Cote, Akos Kadar, Xingdi Yuan, et al.*2018 · 331 citations*](https://md.research.elamur.ai/free/en/papers/cote-2018-textworld-learning-environment-text-based/)
8. [**An Actor-Critic Algorithm for Sequence Prediction**Dzmitry Bahdanau, Philemon Brakel, Kelvin Xu, et al.*2016 · 320 citations*](https://md.research.elamur.ai/free/en/papers/bahdanau-2016-actor-critic-algorithm-sequence-prediction/)
9. [**Masked Language Modeling and the Distributional Hypothesis: Order Word Matters Pre-training for Little**Koustuv Sinha, Robin Jia, Dieuwke Hupkes, et al.*2021 · 292 citations*](https://md.research.elamur.ai/free/en/papers/sinha-2021-masked-language-modeling-distributional-hypothesis/)
10. [**Humanity's Last Exam**Long Phan, Alice Gatti, Ziwen Han, et al.*2025 · 235 citations*](https://md.research.elamur.ai/free/en/papers/phan-2025-humanity-s-last-exam/)
11. [**Factual Error Correction for Abstractive Summarization Models**Meng Cao, Yue Dong, Jiapeng Wu, et al.*2020 · 217 citations*](https://md.research.elamur.ai/free/en/papers/cao-2020-factual-error-correction-abstractive-summarization/)
12. [**An Empirical Survey of the Effectiveness of Debiasing Techniques for Pre-trained Language Models**Nicholas Meade, Elinor Poole-Dayan, Siva Reddy*2022 · 211 citations*](https://md.research.elamur.ai/free/en/papers/meade-2022-empirical-survey-effectiveness-debiasing-techniques/)
13. [**On the Origin of Hallucinations in Conversational Models: Is it the Datasets or the Models?**Nouha Dziri, Sivan Milton, Mo Yu, et al.*2022 · 205 citations*](https://md.research.elamur.ai/free/en/papers/dziri-2022-origin-hallucinations-conversational-models-is/)
14. [**Improving Passage Retrieval with Zero-Shot Question Generation**Devendra Sachan, Mike Lewis, Mandar Joshi, et al.*2022 · 197 citations*](https://md.research.elamur.ai/free/en/papers/sachan-2022-improving-passage-retrieval-zero-shot/)
15. [**Global MMLU: Understanding and Addressing Cultural and Linguistic Biases in Multilingual Evaluation**Shivalika Singh, Angelika Romanou, Clémentine Fourrier, et al.*2025 · 179 citations*](https://md.research.elamur.ai/free/en/papers/singh-2025-global-mmlu-understanding-addressing-cultural/)
16. [**Towards Leaving No Indic Language Behind: Building Monolingual Corpora, Benchmark and Models for Indic Languages**Sumanth Doddapaneni, Rahul Aralikatte, Gowtham Ramesh, et al.*2023 · 174 citations*](https://md.research.elamur.ai/free/en/papers/doddapaneni-2023-leaving-no-indic-language-behind/)
17. [**Hallucinated but Factual! Inspecting the Factuality of Hallucinations in Abstractive Summarization**Meng Cao, Yue Dong, Jackie Cheung*2022 · 167 citations*](https://md.research.elamur.ai/free/en/papers/cao-2022-hallucinated-but-factual-inspecting-factuality/)
18. [**Are LLMs Breaking MT Metrics? Results of the WMT24 Metrics Shared Task**Markus Freitag, Nitika Mathur, Daniel Deutsch, et al.*2024 · 106 citations*](https://md.research.elamur.ai/free/en/papers/freitag-2024-are-llms-breaking-mt-metrics/)
19. [**Resolving Event Coreference with Supervised Representation Learning and Clustering-Oriented Regularization**Kian Kenyon-Dean, Jackie Chi Kit Cheung, Doina Precup*2018 · 87 citations*](https://md.research.elamur.ai/free/en/papers/dean-2018-resolving-event-coreference-supervised-representation/)
20. [**UniMorph 4.0: Universal Morphology**Khuyagbaatar Batsuren, Omer Goldman, Salam Khalifa*2022 · 82 citations*](https://md.research.elamur.ai/free/en/papers/batsuren-2022-unimorph-4/)
21. [**The KnowRef Coreference Corpus: Removing Gender and Number Cues for Difficult Pronominal Anaphora Resolution**Ali Emami, Paul Trichelair, Adam Trischler, et al.*2019 · 73 citations*](https://md.research.elamur.ai/free/en/papers/emami-2019-knowref-coreference-corpus-removing-gender/)
22. [**Verb Phrase Ellipsis Resolution Using Discriminative and Margin-Infused Algorithms**Kian Kenyon-Dean, Jackie Chi Kit Cheung, Doina Precup*2016 · 24 citations*](https://md.research.elamur.ai/free/en/papers/dean-2016-verb-phrase-ellipsis-resolution-discriminative/)
23. [**The More Antecedents, the Merrier: Resolving Multi-Antecedent Anaphors**Hardik Vala, Andrew Piper, Derek Ruths*2016 · 19 citations*](https://md.research.elamur.ai/free/en/papers/vala-2016-more-antecedents-merrier-resolving-multi/)
24. [**SSA-COMET: Do LLMs Outperform Learned Metrics in Evaluating MT for Under-Resourced African Languages?**Senyu Li, Jiayi Wang, Felermino D. M. A. Ali, et al.*2025 · 13 citations*](https://md.research.elamur.ai/free/en/papers/li-2025-ssa-comet-do-llms-outperform/)
25. [**Multilingual Gradient Word-Order Typology from Universal Dependencies**Emi Baylor, Esther Ploeger, Johannes Bjerva*2024 · 12 citations*](https://md.research.elamur.ai/free/en/papers/baylor-2024-multilingual-gradient-word-order-typology/)
26. [**Are Frequent Phrases Directly Retrieved like Idioms? An Investigation with Self-Paced Reading and Language Models**Giulia Rambelli, Emmanuele Chersoni, Marco S. G. Senaldi, et al.*2023 · 11 citations*](https://md.research.elamur.ai/free/en/papers/rambelli-2023-are-frequent-phrases-directly-retrieved/)
27. [**Let's do it "again": A First Computational Approach to Detecting Adverbial Presupposition Triggers**Andre Cianflone, Yulan Feng, Jad Kabbara, et al.*2018 · 10 citations*](https://md.research.elamur.ai/free/en/papers/cianflone-2018-let-s-do-it-again/)
28. [**Challenges to Evaluating the Generalization of Coreference Resolution Models: A Measurement Modeling Perspective**Ian Porada, Alexandra Olteanu, Kaheer Suleman, et al.*2024 · 10 citations*](https://md.research.elamur.ai/free/en/papers/porada-2024-challenges-evaluating-generalization-coreference-resolution/)
29. [**Unsupervised Classification of English Words Based on Phonological Information: Discovery of Germanic and Latinate Clusters**Takashi Morita, Timothy J. O'Donnell*2025 · 1 citation*](https://md.research.elamur.ai/free/en/papers/morita-2025-unsupervised-classification-english-words-based/)
30. [**Frozen by context: Pragmatic factors of syntactic freezing**Gouming Martens*2021*](https://md.research.elamur.ai/free/en/papers/martens-2021-frozen-context-pragmatic-factors-syntactic/)
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