The DOI matches its Crossref record; 45 of the 45 references it cites were found in a registry or an index.
Linguistically-Informed Self-Attention for Semantic Role Labeling
Open accessAbstract
Current state-of-the-art semantic role labeling (SRL) uses a deep neural network with no explicit linguistic features.However, prior work has shown that gold syntax trees can dramatically improve SRL decoding, suggesting the possibility of increased accuracy from explicit modeling of syntax.In this work, we present linguistically-informed self-attention (LISA): a neural network model that combines multi-head self-attention with multi-task learning across dependency parsing, part-ofspeech tagging, predicate detection and SRL.Unlike previous models which require significant pre-processing to prepare linguistic features, LISA can incorporate syntax using merely raw tokens as input, encoding the sequence only once to simultaneously perform parsing, predicate detection and role labeling for all predicates.Syntax is incorporated by training one attention head to attend to syntactic parents for each token.Moreover, if a high-quality syntactic parse is already available, it can be beneficially injected at test time without re-training our SRL model.In experiments on CoNLL-2005 SRL, LISA achieves new state-of-the-art performance for a model using predicted predicates and standard word embeddings, attaining 2.5 F1 absolute higher than the previous state-of-the-art on newswire and more than 3.5 F1 on outof-domain data, nearly 10% reduction in error.On ConLL-2012 English SRL we also show an improvement of more than 2.5 F1.LISA also out-performs the state-of-the-art with contextually-encoded (ELMo) word representations, by nearly 1.0 F1 on news and more than 2.0 F1 on out-of-domain text.
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Deep Contextualized Word Representations
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Cited by
Energy and Policy Considerations for Deep Learning in NLP
Strubell, E., Ganesh, A., & McCallum, A. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1355 · Cited by 3,684
Self-citationMultimodal Transformer for Unaligned Multimodal Language Sequences
Tsai, Y.-H. H., Bai, S., Liang, P. P., Kolter, J. Z., Morency, L.-P., & Salakhutdinov, R. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1656 · Cited by 2,259
“…have also been successfully applied to other tasks, including language modeling (Dai et al., 2018; Baevski and Auli, 2019), semantic role labeling (Strubell et al., 2018), word sense disambiguation (Tang et al., 2018), learning sentence representations (Devlin et al., 2018), and video activity…”
MethodWhat Does BERT Look at? An Analysis of BERT’s Attention
Clark, K., Khandelwal, U., Levy, O., & Manning, C. D. (2019) · Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP · doi:10.18653/v1/w19-4828 · Cited by 1,373
Energy and Policy Considerations for Modern Deep Learning Research
Strubell, E., Ganesh, A., & McCallum, A. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i09.7123 · Cited by 774
Self-citationEmergent linguistic structure in artificial neural networks trained by self-supervision
Manning, C. D., Clark, K., Hewitt, J., Khandelwal, U., & Levy, O. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1907367117 · Cited by 339
Metadata
- Authors
- Strubell, E., Verga, P., Andor, D., Weiss, D., & McCallum, A.
- Venue
- Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
- Published
- 2018
- Pages
- 5027-5038
- OpenAlex
- W2798638375
- Cited by
- 406
- References
- 45
- FWCI
- 30.59
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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