Anders Søgaard

Anders Søgaard

Professor


  1. 2019
  2. Udgivet

    Multi-Task Semantic Dependency Parsing with Policy Gradient for Learning Easy-First Strategies

    Kurita, S. & Søgaard, Anders, 2019, Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, s. 2420-2430

    Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

  3. Udgivet

    Jointly Learning to Label Sentences and Tokens

    Rei, M. & Søgaard, Anders, 2019, Proceedings of 33nd AAAI Conference on Artificial Intelligence, AAAI 2019. AAAI Press, s. 6916-6923

    Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

  4. Udgivet

    A Survey of Cross-lingual Word Embedding Models

    Ruder, S., Vulić, I. & Søgaard, Anders, 2019, I: The Journal of Artificial Intelligence Research. 65, s. 569-631

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt

  5. Udgivet

    Latent Multi-Task Architecture Learning

    Ruder, S., Bingel, J., Augenstein, Isabelle & Søgaard, Anders, 2019, Proceedings of 33nd AAAI Conference on Artificial Intelligence, AAAI 2019. AAAI Press, s. 4822-4829

    Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

  6. Udgivet

    Cross-Lingual Word Embeddings

    Søgaard, Anders, Vulić, I., Ruder, S. & Faruqui, M., 2019, 2 udg. Morgan & Claypool Publishers. 132 s. (Synthesis Lectures on Human Language Technologies).

    Publikation: Bog/antologi/afhandling/rapportBogForskningfagfællebedømt

  7. Udgivet

    A systematic comparison of methods for low-resource dependency parsing on genuinely low-resource languages

    Vania, C., Kementchedjhieva, Yova Radoslavova, Søgaard, Anders & Lopez, A., 2019, Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, s. 1105-1116

    Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

  8. Udgivet

    Better, Faster, Stronger Sequence Tagging Constituent Parsers

    Vilares, D., Abdou, M. & Søgaard, Anders, 2019, Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Association for Computational Linguistics, s. 3372-3383

    Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

  9. Udgivet

    Higher-order Comparisons of Sentence Encoder Representations

    vqc439, V., Kulmizev, A., Hill, F., Low, D. M. L. & Søgaard, Anders, 2019, Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing. Association for Computational Linguistics, s. 5838–5845

    Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

  10. 2018
  11. Udgivet

    Multi-task Learning of Pairwise Sequence Classification Tasks Over Disparate Label Spaces

    Augenstein, Isabelle, Ruder, S. & Søgaard, Anders, 2018, Proceedings, 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies : (Long Papers). Association for Computational Linguistics, Bind 1. s. 1896–1906 11 s.

    Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

  12. Udgivet

    Sequence classification with human attention

    Barrett, M. J., Bingel, J., Hollenstein, N., Rei, M. & Søgaard, Anders, 2018, Proceedings of the 22nd Conference on Computational Natural Language Learning (CoNLL 2018). Korhonen, A. & Titov, I. (red.). Association for Computational Linguistics, s. 302–312

    Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

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