Anders Søgaard
Professor
Natural Language Processing
Universitetsparken 1
2100 København Ø
Institut for Kommunikation
Karen Blixens Plads 8
2300 København S
- 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. 5838–5845Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
- 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. 3372-3383Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
- 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. 1105-1116Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
- 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/rapport › Bog › Forskning › fagfællebedømt
- 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. 569-631Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
- 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. 4822-4829Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
- 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. 6916-6923Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
- 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. 2420-2430Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
- Udgivet
Lost in Evaluation: Misleading Benchmarks for Bilingual Dictionary Induction
Kementchedjhieva, Yova Radoslavova, lwp876, 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. 3336–3341Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
- Udgivet
Comparing Unsupervised Word Translation Methods Step by Step
Hartmann, M., Kementchedjhieva, Yova Radoslavova & Søgaard, Anders, 2019, Advances in Neural Information Processing Systems 32 (NIPS 2019). 11 s.Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
ID: 13266
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Multilingual projection for parsing truly low resource languages
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Multi-task Learning of Pairwise Sequence Classification Tasks Over Disparate Label Spaces
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Reading metrics for estimating task efficiency with MT output
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