Syntax-informed Question Answering with Heterogeneous Graph Transformer
This work addresses the challenge of improving question answering models for natural language processing by integrating linguistics knowledge in an incremental way.
The paper tackled the problem of enhancing pre-trained language models for question answering by incorporating explicit syntactic information without retraining from scratch, using a heterogeneous graph transformer to encode dependency and constituency structures, and demonstrated competitive performance on the Stanford Question Answering Dataset compared to BERT.
Large neural language models are steadily contributing state-of-the-art performance to question answering and other natural language and information processing tasks. These models are expensive to train. We propose to evaluate whether such pre-trained models can benefit from the addition of explicit linguistics information without requiring retraining from scratch. We present a linguistics-informed question answering approach that extends and fine-tunes a pre-trained transformer-based neural language model with symbolic knowledge encoded with a heterogeneous graph transformer. We illustrate the approach by the addition of syntactic information in the form of dependency and constituency graphic structures connecting tokens and virtual vertices. A comparative empirical performance evaluation with BERT as its baseline and with Stanford Question Answering Dataset demonstrates the competitiveness of the proposed approach. We argue, in conclusion and in the light of further results of preliminary experiments, that the approach is extensible to further linguistics information including semantics and pragmatics.