Joint Event and Temporal Relation Extraction with Shared Representations and Structured Prediction

By Rujun Han, Qiang Ning, Nanyun Peng. 2019

134 citations recorded by the source corpus.

We propose a joint event and temporal relation extraction model with shared representation learning and structured prediction. The proposed method has two advantages over existing work. First, it improves event representation by allowing the event and relation modules to share the same contextualized embeddings and neural representation learner. Second, it avoids error propagation in the conventional pipeline systems by leveraging structured inference and learning methods to assign both the event labels and the temporal relation labels jointly. Experiments show that the proposed method can improve both event extraction and temporal relation extraction over state-of-the-art systems, with the end-to-end F1 improved by 10% and 6.8% on two benchmark datasets respectively.

Subfields: Artificial Intelligence, Information Systems

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. Xiang Ren
2. Ashish Sabharwal
3. Nanyun Peng
4. Tushar Khot
5. Dan Roth
6. Kyle Richardson
7. Heng Ji
8. Rujun Han

In Information Systems

1. Heng Ji
2. Ashish Sabharwal
3. Tushar Khot
4. Nanyun Peng
5. Rujun Han
6. Shuaicheng Zhang
7. Lifu Huang
8. Xiang Ren

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.”
• Temporal Reasoning on Implicit Events from Distant Supervision (2020, background): “Multiple systems have been proposed as part of research into temporal ordering (Do et al., 2012; Moens and Leeuwenberg, 2017; Leeuwenberg and Moens, 2018; Meng and Rumshisky, 2018; Ning et al., 2018c; Han et al., 2019), duration prediction (Vashishtha et al., 2019) and other tasks.”
• Joint Constrained Learning for Event-Event Relation Extraction (2020, methodology): “R T thereof denotes the set of temporal relations defined in the literature (Ning et al., 2017b, 2018b, 2019; Han et al., 2019b), which contains B EFORE , A FTER , E QUAL , and V AGUE .”
• Domain Knowledge Empowered Structured Neural Net for End-to-End Event Temporal Relation Extraction (2020, unknown): “As illustrated in the left column in Figure 2, our end-to-end model shares a similar work-flow as the pipeline model in Han et al. (2019b), where multi-task learning with a shared feature extractor is used to train the pipeline model.”
• Clinical Temporal Relation Extraction with Probabilistic Soft Logic Regularization and Global Inference (2020, unknown): “CTRL-PG and SP-ILP both fail to label any instance as Simultaneous because of its even fewer instances (1.5”
• Extracting Temporal Event Relation with Syntactic-Guided Temporal Graph Transformer (2021, methodology): “Then, following previous studies (Ning et al., 2019b,a; Han et al., 2019b; Wang et al., 2020a), we consider each pair of event mentions that are detected from one or two continuous sentences, and predict their temporal relationship.”
• A Joint Neural Model for Information Extraction with Global Features (2020, methodology): “, 2019) and event and temporal relation extraction (Han et al., 2019).”