Joint Constrained Learning for Event-Event Relation Extraction
By Haoyu Wang, Muhao Chen, Hongming Zhang, Dan Roth. 2020
133 citations recorded by the source corpus.
Understanding natural language involves recognizing how multiple event mentions structurally and temporally interact with each other. In this process, one can induce event complexes that organize multi-granular events with temporal order and membership relations interweaving among them. Due to the lack of jointly labeled data for these relational phenomena and the restriction on the structures they articulate, we propose a joint constrained learning framework for modeling event-event relations. Specifically, the framework enforces logical constraints within and across multiple temporal and subevent relations by converting these constraints into differentiable learning objectives. We show that our joint constrained learning approach effectively compensates for the lack of jointly labeled data, and outperforms SOTA methods on benchmarks for both temporal relation extraction and event hierarchy construction, replacing a commonly used but more expensive global inference process. We also present a promising case study showing the effectiveness of our approach in inducing event complexes on an external corpus.
Subfields: Artificial Intelligence
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In Artificial Intelligence
- Xiang Ren
- Ashish Sabharwal
- Tushar Khot
- Nanyun Peng
- Kyle Richardson
- Dan Roth
- Rujun Han
- Daniel Khashabi
Citations in the corpus
- ECONET: Effective Continual Pretraining of Language Models for Event Temporal Reasoning (2020, background): “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…”
- Temporal Reasoning on Implicit Events from Distant Supervision (2020, methodology): “We see that our model is consistently better than BaseLM, and at the same time, comparable to Wang et al. (2020).”
- Extracting Temporal Event Relation with Syntactic-Guided Temporal Graph Transformer (2021, methodology): “Recently, deep neural networks (Dligach et al., 2017; Tourille et al., 2017) and large-scale pre-training language models (Han et al., 2019a, 2020; Wang et al., 2020a; Zhou et al., 2020) are further employed and show state-of-the-art performance on temporal relation extraction.”