Predicate Representations and Polysemy in VerbNet Semantic Parsing

James Gung, Martha Palmer


Abstract
Despite recent advances in semantic role labeling propelled by pre-trained text encoders like BERT, performance lags behind when applied to predicates observed infrequently during training or to sentences in new domains. In this work, we investigate how role labeling performance on low-frequency predicates and out-of-domain data can be further improved by using VerbNet, a verb lexicon that groups verbs into hierarchical classes based on shared syntactic and semantic behavior and defines semantic representations describing relations between arguments. We find that VerbNet classes provide an effective level of abstraction, improving generalization on low-frequency predicates by allowing them to learn from the training examples of other predicates belonging to the same class. We also find that joint training of VerbNet role labeling and predicate disambiguation of VerbNet classes for polysemous verbs leads to improvements in both tasks, naturally supporting the extraction of VerbNet’s semantic representations.
Anthology ID:
2021.iwcs-1.6
Volume:
Proceedings of the 14th International Conference on Computational Semantics (IWCS)
Month:
June
Year:
2021
Address:
Groningen, The Netherlands (online)
Venue:
IWCS
SIG:
SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
51–62
Language:
URL:
https://aclanthology.org/2021.iwcs-1.6
DOI:
Bibkey:
Cite (ACL):
James Gung and Martha Palmer. 2021. Predicate Representations and Polysemy in VerbNet Semantic Parsing. In Proceedings of the 14th International Conference on Computational Semantics (IWCS), pages 51–62, Groningen, The Netherlands (online). Association for Computational Linguistics.
Cite (Informal):
Predicate Representations and Polysemy in VerbNet Semantic Parsing (Gung & Palmer, IWCS 2021)
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PDF:
https://aclanthology.org/2021.iwcs-1.6.pdf
Code
 jgung/verbnet-parsing-iwcs-2021