Publication: Joint training with semantic role labeling for better generalization in natural language inference
Program
KU-Authors
KU Authors
Co-Authors
Publication Date
Language
Embargo Status
Journal Title
Journal ISSN
Volume Title
Alternative Title
Abstract
End-to-end models trained on natural language inference (NLI) datasets show low generalization on out-of-distribution evaluation sets. The models tend to learn shallow heuristics due to dataset biases. The performance decreases dramatically on diagnostic sets measuring compositionality or robustness against simple heuristics. Existing solutions for this problem employ dataset augmentation which has the drawbacks of being applicable to only a limited set of adversaries and at worst hurting the model performance on other adversaries not included in the augmentation set. Our proposed solution is to improve sentence understanding (hence out-of-distribution generalization) with joint learning of explicit semantics. We show that a BERT based model trained jointly on English semantic role labeling (SRL) and NLI achieves significantly higher performance on external evaluation sets measuring generalization performance.
Source
Publisher
Assoc Computational Linguistics-Acl
Subject
Computer science, Artificial intelligence, Computer science, Linguistics
Citation
Has Part
Source
5th Workshop On Representation Learning For Nlp (Repl4nlp-2020)