Koustuv Sinha

Koustuv Sinha

Research Scientist

Meta AI

My current research interest involves investigating the limits of systematic language understanding of modern neural language representation systems, by leveraging linguistic and logical priors. In particular, I investigate how language is represented by neural models in a human-like way by testing their ability to understand the semantics, syntax and generalizability in the context of natural and artificial languages. I am also interested in improving the state of Generative Language Modeling (Dialog Systems).

I’m a Research Scientist at Meta AI New York, in the Fundamental AI Research (FAIR) team. I did my PhD from McGill University (School of Computer Science) and Mila (Quebec AI Institute), supervised by Joelle Pineau, in the wonderful city of Montreal, QC, Canada. I spent a significant portion of my PhD being a Research Intern (STE) at Meta AI (FAIR), Montreal.

I am an associate editor of ReScience C, a peer reviewed journal promoting reproducible research, and I am the lead organizer of the annual Machine Learning Reproducibility Challenge (V1, V2, V3, V4, V5 ). My work has been covered by several news outlets in the past, including Nature, VentureBeat, InfoQ, DailyMail and Hindustan Times.

Resumé | PhD Thesis

  • Machine Learning
  • Natural Language Processing
  • Computational Linguistics
  • PhD in Computer Science (ML & NLP), 2022

    McGill University

  • MSc in Computer Science (ML & NLP), 2018

    McGill University

  • B.Tech in Computer Science, 2014

    West Bengal University of Technology

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Recent Publications

(2022). The Curious Case of Absolute Position Embeddings. Findings of Empirical Methods of Natural Language Processing.

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(2022). How sensitive are translation systems to extra contexts? Mitigating gender bias in Neural Machine Translation models through relevant contexts. Findings of Empirical Methods of Natural Language Processing (EMNLP), 2022.

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(2021). Masked Language Modeling and the Distributional Hypothesis: Order Word Matters Pre-training for Little. Empirical Methods of Natural Language Processing (EMNLP).

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