Domain Knowledge Empowered Structured Neural Net for End-to-End Event Temporal Relation Extraction

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, pages 5717–5729, November 16–20, 2020. c⃝2020 Association for Computational Linguistics 5717 Domain Knowledge Empowered Structured Neural Net for End-to-End Event Temporal Relation Extraction Rujun Han1,2 Yichao Zhou3 Nanyun Peng1,2,3 1Information Sciences Institute, University of Southern California 2Department of Computer Science, University of Southern California 3Department of Computer Science, University of California, Los Angeles rujunhan@isi.edu; yz@cs.ucla.edu; violetpeng@cs.ucla.edu Abstract Extracting event temporal relations is a criti- cal task for information extraction and plays an important role in natural language under- standing. Prior systems leverage deep learn- ing and pre-trained language models to im- prove the performance of the task. However, these systems often suffer from two shortcom- ings: 1) when performing maximum a posteri- ori (MAP) inference based on neural models, previous systems only used structured knowl- edge that is assumed to be absolutely correct, i.e., hard constraints; 2) biased predictions on dominant temporal relations when training with a limited amount of data. To address these issues, we propose a framework that enhances deep neural network with distributional con- straints constructed by probabilistic domain knowledge. We solve the constrained infer- ence problem via Lagrangian Relaxation and apply it to end-to-end event temporal relation extraction tasks. Experimental results show our framework is able to improve the baseline neural network models with strong statistical significance on two widely used datasets in news and clinical domains. 1 Introduction Extracting event temporal relations from raw text data has attracted surging attention in the NLP re- search community in recent years as it is a funda- mental task for commonsense reasoning and nat- ural language understanding. It facilitates various downstream applications, such as forecasting social events and tracking patients’ medical history. Fig- ure 1 shows an example of this task where an event extractor first needs to identify events (buildup, say and stop) in the input and then a relation clas- sifier predicts all pairwise relations among them, resulting in a temporal ordering as illustrated in the figure. For example, say is BEFORE stop; buildup INCLUDES say; the temporal ordering Figure 1: An example of the event temporal order- ing task. Text input is taken from the news dataset in our experiments. Solid lines / arrows between two highlighted events show their gold temporal relations, e.g. say BEFORE stop and buildup INCLUDES say, and the dash line shows a wrong prediction, i.e., the VAGUE relation between buildup and say. In the table, Column Overall shows the relation distribution over the entire training corpus; Column Type Pair (P) shows the predicted relation distribution condition on the event pairs having types occurrence and reporting (such as buildup and say); Column Type Pair (G) shows the gold relation distribution condition on event pairs having the same types. Biased predictions of VAGUE relation between buildup and say can be par- tially corrected by using the gold event type-relation statistics in Column Type Pair (G). between buildup and stop cannot be decided from the context, so the relation should be …

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