Anders Søgaard

Anders Søgaard

Professor


  1. 2020
  2. Udgivet

    Weakly Supervised POS Taggers Perform Poorly on Truly Low-Resource Languages

    Kann, K., Lacroix, O. & Søgaard, Anders, 2020, Proceedings of the AAAI Conference on Artificial Intelligence (AAAI 2020): [AAAI-20 Technical Tracks 5]. AAAI Press, s. 8066-8073.

    Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

  3. Udgivet

    What Do You Mean ‘Why?’: Resolving Sluices in Conversation

    Hansen, V. P. B. & Søgaard, Anders, 2020, Proceedings of the AAAI Conference on Artificial Intelligence (AAAI 2020): [AAAI-20 Technical Tracks 5]. AAAI Press, s. 7887-7894.

    Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

  4. Udgivet

    WikiBank: Using wikidata to improve multilingual frame-semantic parsing

    Sas, C., Beloucif, M. & Søgaard, Anders, 2020, LREC 2020 - 12th International Conference on Language Resources and Evaluation, Conference Proceedings. Calzolari, N., Bechet, F., Blache, P., Choukri, K., Cieri, C., Declerck, T., Goggi, S., Isahara, H., Maegaard, B., Mariani, J., Mazo, H., Moreno, A., Odijk, J. & Piperidis, S. (red.). European Language Resources Association (ELRA), s. 4183-4189

    Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

  5. 2021
  6. Udgivet

    A Global–Local Attentive Relation Detection Model for Knowledge-Based Question Answering

    Qiu, C., Zhou, G., Cai, Z. & Søgaard, Anders, 2021, I: IEEE Transactions on Artificial Intelligence. 2, 2, s. 200-212

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt

  7. Udgivet

    A Multilingual Benchmark for Probing Negation-Awareness with Minimal Pairs

    Hartmann, M., de Lhoneux, M., Hershcovich, Daniel, Kementchedjhieva, Yova Radoslavova, Nielsen, Lukas Christian, Qiu, C. & Søgaard, Anders, 2021, Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, s. 244–257

    Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

  8. Udgivet

    Analogy Training Multilingual Encoderss

    Garneau, N., lwp876, L., Sandholm, A., Ruder, S., Vulić, I. & Søgaard, Anders, 2021, Proceedings of the AAAI-21 International Joint Conference on Artificial Intelligence. AAAI Press, s. 12884-12892. 10 s. (Proceedings of the International Joint Conference on Artificial Intelligence; Nr. 14, Bind 35).

    Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

  9. Udgivet

    Attention can reflect syntactic structure (if you let it)

    Ravishankar, V., Kulmizev, A., Abdou, M., Søgaard, Anders & Nivre, J., 2021, EACL 2021 - 16th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference. Association for Computational Linguistics, s. 3031-3045

    Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

  10. Udgivet

    Can Language Models Encode Perceptual Structure Without Grounding? A Case Study in Color

    Abdou, M., Kulmizev, A., Hershcovich, Daniel, Frank, S., Pavlick, E. & Søgaard, Anders, 2021, Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, s. 109–132

    Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

  11. Udgivet

    Clustering Monolingual Vocabularies to Improve Cross-Lingual Generalization

    Bassani, R., Søgaard, Anders & Deoskar, T., 2021, Proceedings of the 1st Workshop on Multilingual Representation Learning. Association for Computational Linguistics, s. 32–40

    Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

  12. Udgivet

    Common Sense Bias in Semantic Role Labeling

    Lent, H. C. & Søgaard, Anders, 2021, Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021). Association for Computational Linguistics, s. 114–119

    Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

ID: 13266