Hayley Ross, Ameya Sunil Mahabaleshwarkar, Yoshi Suhara. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

Extracting temporal relations between events and time expressions has many applications such as constructing event timelines and timerelated 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.