Specializing Unsupervised Pretraining Models for Word-Level Semantic Similarity

Anne Lauscher, Ivan Vulić, Edoardo Maria Ponti, Anna Korhonen, Goran Glavaš


Abstract
Unsupervised pretraining models have been shown to facilitate a wide range of downstream NLP applications. These models, however, retain some of the limitations of traditional static word embeddings. In particular, they encode only the distributional knowledge available in raw text corpora, incorporated through language modeling objectives. In this work, we complement such distributional knowledge with external lexical knowledge, that is, we integrate the discrete knowledge on word-level semantic similarity into pretraining. To this end, we generalize the standard BERT model to a multi-task learning setting where we couple BERT’s masked language modeling and next sentence prediction objectives with an auxiliary task of binary word relation classification. Our experiments suggest that our “Lexically Informed” BERT (LIBERT), specialized for the word-level semantic similarity, yields better performance than the lexically blind “vanilla” BERT on several language understanding tasks. Concretely, LIBERT outperforms BERT in 9 out of 10 tasks of the GLUE benchmark and is on a par with BERT in the remaining one. Moreover, we show consistent gains on 3 benchmarks for lexical simplification, a task where knowledge about word-level semantic similarity is paramount, as well as large gains on lexical reasoning probes.
Anthology ID:
2020.coling-main.118
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
1371–1383
Language:
URL:
https://aclanthology.org/2020.coling-main.118
DOI:
10.18653/v1/2020.coling-main.118
Bibkey:
Cite (ACL):
Anne Lauscher, Ivan Vulić, Edoardo Maria Ponti, Anna Korhonen, and Goran Glavaš. 2020. Specializing Unsupervised Pretraining Models for Word-Level Semantic Similarity. In Proceedings of the 28th International Conference on Computational Linguistics, pages 1371–1383, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Cite (Informal):
Specializing Unsupervised Pretraining Models for Word-Level Semantic Similarity (Lauscher et al., COLING 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.coling-main.118.pdf
Data
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