Positional Attention-based Frame Identification with BERT: A Deep Learning Approach to Target Disambiguation and Semantic Frame Selection
This addresses the problem of robust frame identification for both in-domain and out-of-domain data in semantic parsing, which is incremental as it builds on existing methods with BERT and attention.
The paper tackled the frame identification subtask in frame-semantic parsing by proposing PAFIBERT, a deep neural network combining BERT with a position-based attention mechanism, which achieved new state-of-the-art results on in-domain and out-of-domain benchmark test sets.
Semantic parsing is the task of transforming sentences from natural language into formal representations of predicate-argument structures. Under this research area, frame-semantic parsing has attracted much interest. This parsing approach leverages the lexical information defined in FrameNet to associate marked predicates or targets with semantic frames, thereby assigning semantic roles to sentence components based on pre-specified frame elements in FrameNet. In this paper, a deep neural network architecture known as Positional Attention-based Frame Identification with BERT (PAFIBERT) is presented as a solution to the frame identification subtask in frame-semantic parsing. Although the importance of this subtask is well-established, prior research has yet to find a robust solution that works satisfactorily for both in-domain and out-of-domain data. This study thus set out to improve frame identification in light of recent advancements of language modeling and transfer learning in natural language processing. The proposed method is partially empowered by BERT, a pre-trained language model that excels at capturing contextual information in texts. By combining the language representation power of BERT with a position-based attention mechanism, PAFIBERT is able to attend to target-specific contexts in sentences for disambiguating targets and associating them with the most suitable semantic frames. Under various experimental settings, PAFIBERT outperformed existing solutions by a significant margin, achieving new state-of-the-art results for both in-domain and out-of-domain benchmark test sets.