Exploring Contextualized Neural Language Models for Temporal Dependency Parsing
This work addresses the problem of extracting temporal relations for applications like event timelines, but it is incremental as it builds on existing BERT models.
The paper tackled temporal dependency parsing by developing BERT-based variants, showing that BERT significantly improves performance over previous methods, with concrete gains reported.
Extracting temporal relations between events and time expressions has many applications such as constructing event timelines and time-related question answering. It is a challenging problem which requires syntactic and semantic information at sentence or discourse levels, which may be captured by deep contextualized language models (LMs) such as BERT (Devlin et al., 2019). In this paper, we develop several variants of BERT-based temporal dependency parser, and show that BERT significantly improves temporal dependency parsing (Zhang and Xue, 2018a). We also present a detailed analysis on why deep contextualized neural LMs help and where they may fall short. Source code and resources are made available at https://github.com/bnmin/tdp_ranking.