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arXiv · 1909.00429

An Improved Neural Baseline for Temporal Relation Extraction

Qiang Ning·Sanjay Subramanian·Dan Roth
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Abstract

Determining temporal relations (e.g., before or after) between events has been a challenging natural language understanding task, partly due to the difficulty to generate large amounts of high-quality training data. Consequently, neural approaches have not been widely used on it, or showed only moderate improvements. This paper proposes a new neural system that achieves about 10% absolute improvement in accuracy over the previous best system (25% error reduction) on two benchmark datasets. The proposed system is trained on the state-of-the-art MATRES dataset and applies contextualized word embeddings, a Siamese encoder of a temporal common sense knowledge base, and global inference via integer linear programming (ILP). We suggest that the new approach could serve as a strong baseline for future research in this area.

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Qiang Ning, Sanjay Subramanian, Dan Roth. 2019-09-01. An Improved Neural Baseline for Temporal Relation Extraction. https://arxiv.org/abs/1909.00429

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