Capturing Argument Interaction in Semantic Role Labeling with Capsule Networks

Xinchi Chen, Chunchuan Lyu, Ivan Titov

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

Semantic role labeling (SRL) involves extracting propositions (i.e. predicates and their typed arguments) from natural language sentences. State-of-the-art SRL models rely on powerful encoders (e.g., LSTMs) and do not model non-local interaction between arguments. We propose a new approach to modeling these interactions while maintaining efficient inference. Specifically, we use Capsule Networks (Sabour et al., 2017): each proposition is encoded as a tuple of capsules, one capsule per argument type (i.e. role). These tuples serve as embeddings of entire propositions. In every network layer, the capsules interact with each other and with representations of words in the sentence. Each iteration results in updated proposition embeddings and updated predictions about the SRL structure. Our model substantially outperforms the nonrefinement baseline model on all 7 CoNLL-2009 languages and achieves state-of-the-art results on 5 languages (including English) for dependency SRL. We analyze the types of mistakes corrected by the refinement procedure. For example, each role is typically (but not always) filled with at most one argument. Whereas enforcing this approximate constraint is not useful with the modern SRL system, iterative procedure corrects the mistakes by capturing this intuition in a flexible and contextsensitive way.
Original languageEnglish
Title of host publicationProceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
PublisherAssociation for Computational Linguistics
Pages5418-5428
Number of pages11
ISBN (Print)978-1-950737-90-1
DOIs
Publication statusPublished - 3 Nov 2019
Event2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing - Hong Kong, Hong Kong
Duration: 3 Nov 20197 Nov 2019
https://www.emnlp-ijcnlp2019.org/

Conference

Conference2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing
Abbreviated titleEMNLP-IJCNLP 2019
Country/TerritoryHong Kong
CityHong Kong
Period3/11/197/11/19
Internet address

Fingerprint

Dive into the research topics of 'Capturing Argument Interaction in Semantic Role Labeling with Capsule Networks'. Together they form a unique fingerprint.

Cite this