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Researcher profile

Veselin Stoyanov

6 papers in the PaperMetrix corpus

Publications

Papers by this author

  1. Continual Few-Shot Learning for Text Classification

    2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

    Natural Language Processing (NLP) is increasingly relying on general end-to-end systems that need to handle many different linguistic phenomena and nuances. For example, a Natural Language Inference (NLI) system has to recognize sentiment, handle numbers, …

  2. XNLI: Evaluating Cross-lingual Sentence Representations

    2018 · arXiv (Cornell University)

    Alexis Conneau, Ruty Rinott, Guillaume Lample, Adina Williams, Samuel Bowman, Holger Schwenk, Veselin Stoyanov. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 2018.

  3. HISTORIAE, History of Socio-Cultural Transformation as Linguistic Data Science. A Humanities Use Case

    2019 · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics)

    Given a combinatorial optimisation problem, there are typically multiple ways of modelling it for presentation to an automated solver. Choosing the right combination of model and target solver can have a significant impact on the …

  4. Unsupervised Cross-lingual Representation Learning at Scale

    2020

    Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer, Veselin Stoyanov. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 2020.

  5. Pretrained Encyclopedia: Weakly Supervised Knowledge-Pretrained Language\n Model

    2019 · arXiv (Cornell University)

    Recent breakthroughs of pretrained language models have shown the\neffectiveness of self-supervised learning for a wide range of natural language\nprocessing (NLP) tasks. In addition to standard syntactic and semantic NLP\ntasks, pretrained models achieve strong improvements on …

  6. Emerging Cross-lingual Structure in Pretrained Language Models

    2020

    We study the problem of multilingual masked language modeling, i.e. the training of a single model on concatenated text from multiple languages, and present a detailed study of several factors that influence why these models …