Exploiting Abstract Meaning Representation for Open-Domain Question
  Answering
Cunxiang Wang Zhikun Xu Qipeng Guo Xiangkun Hu Xuefeng Bai Zheng Zhang Yue Zhang
The Open-Domain Question Answering (ODQA) task involves retrieving and
subsequently generating answers from fine-grained relevant passages within a
database. Current systems leverage Pretrained Language Models (PLMs) to model
the relationship between questions and passages. However, the diversity in
surface form expressions can hinder the model's ability to capture accurate
correlations, especially within complex contexts. Therefore, we utilize
Abstract Meaning Representation (AMR) graphs to assist the model in
understanding complex semantic information. We introduce a method known as
Graph-as-Token (GST) to incorporate AMRs into PLMs. Results from Natural
Questions (NQ) and TriviaQA (TQ) demonstrate that our GST method can
significantly improve performance, resulting in up to 2.44/3.17 Exact Match
score improvements on NQ/TQ respectively. Furthermore, our method enhances
robustness and outperforms alternative Graph Neural Network (GNN) methods for
integrating AMRs. To the best of our knowledge, we are the first to employ
semantic graphs in ODQA.