Semantics-aware BERT for Language Understanding
This addresses the need for more powerful natural language understanding models, though it is incremental as it builds on BERT with added semantics.
The authors tackled the problem of language representation models lacking structured semantic information by proposing SemBERT, which incorporates contextual semantics from semantic role labeling into BERT, achieving new state-of-the-art or substantial improvements on ten reading comprehension and language inference tasks.
The latest work on language representations carefully integrates contextualized features into language model training, which enables a series of success especially in various machine reading comprehension and natural language inference tasks. However, the existing language representation models including ELMo, GPT and BERT only exploit plain context-sensitive features such as character or word embeddings. They rarely consider incorporating structured semantic information which can provide rich semantics for language representation. To promote natural language understanding, we propose to incorporate explicit contextual semantics from pre-trained semantic role labeling, and introduce an improved language representation model, Semantics-aware BERT (SemBERT), which is capable of explicitly absorbing contextual semantics over a BERT backbone. SemBERT keeps the convenient usability of its BERT precursor in a light fine-tuning way without substantial task-specific modifications. Compared with BERT, semantics-aware BERT is as simple in concept but more powerful. It obtains new state-of-the-art or substantially improves results on ten reading comprehension and language inference tasks.