Contextualized Word Embeddings Enhanced Event Temporal Relation Extraction for Story Understanding

By Rujun Han, Mengyue Liang, Bashar Alhafni, Nanyun Peng. 2019

22 citations recorded by the source corpus.

Learning causal and temporal relationships between events is an important step towards deeper story and commonsense understanding. Though there are abundant datasets annotated with event relations for story comprehension, many have no empirical results associated with them. In this work, we establish strong baselines for event temporal relation extraction on two under-explored story narrative datasets: Richer Event Description (RED) and Causal and Temporal Relation Scheme (CaTeRS). To the best of our knowledge, these are the first results reported on these two datasets. We demonstrate that neural network-based models can outperform some strong traditional linguistic feature-based models. We also conduct comparative studies to show the contribution of adopting contextualized word embeddings (BERT) for event temporal relation extraction from stories. Detailed analyses are offered to better understand the results.

Subfields: Artificial Intelligence

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Nanyun Peng
2. Xiang Ren
3. Rujun Han
4. Aram Galstyan
5. Ralph M. Weischedel
6. Mu Yang
7. I-Hung Hsu
8. Yejin Choi

Citations in the corpus

• ECONET: Effective Continual Pretraining of Language Models for Event Temporal Reasoning (2020, methodology): “Previous SOTA systems on these data leveraged PTLMs and structured learning (Han et al., 2019c; Wang et al., 2020; Zhou et al., 2020c; Han et al., 2020) and have substantially improved model performances, though none of them tack-led the issue of lacking event temporal knowledge in PTLMs. TORQUE…”
• Deep Structured Neural Network for Event Temporal Relation Extraction (2019, methodology): “More recently, neural network-based methods have been employed for event temporal relation extraction (Tourille et al., 2017a; Cheng and Miyao, 2017; Meng et al., 2017; Han et al., 2019a) which achieved impressive results.”