Specializing Unsupervised Pretraining Models for Word-Level Semantic Similarity
This work addresses the problem of enhancing word-level semantic understanding in NLP models for tasks like language understanding and lexical simplification, though it is incremental as it builds on BERT.
The authors tackled the limitation of unsupervised pretraining models like BERT by integrating external lexical knowledge for word-level semantic similarity, resulting in LIBERT outperforming BERT in 9 out of 10 GLUE tasks and showing gains on lexical simplification benchmarks.
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.