Exploring Interpretability in Event Extraction: Multitask Learning of a Neural Event Classifier and an Explanation Decoder

By Zheng Tang, Gus Hahn-Powell, Mihai Surdeanu. 2020

13 citations recorded by the source corpus.

We propose an interpretable approach for event extraction that mitigates the tension between generalization and interpretability by jointly training for the two goals. Our approach uses an encoder-decoder architecture, which jointly trains a classifier for event extraction, and a rule decoder that generates syntactico-semantic rules that explain the decisions of the event classifier. We evaluate the proposed approach on three biomedical events and show that the decoder generates interpretable rules that serve as accurate explanations for the event classifier’s decisions, and, importantly, that the joint training generally improves the performance of the event classifier. Lastly, we show that our approach can be used for semi-supervised learning, and that its performance improves when trained on automatically-labeled data generated by a rule-based system.

Subfields: Artificial Intelligence, Information Retrieval & Search, Natural Language Processing

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. Noah A. Smith
2. Ana Marasovic
3. Sarah Wiegreffe
4. Yejin Choi
5. Mohit Bansal
6. Matthew E. Peters
7. Iz Beltagy
8. Doug Downey

In Information Retrieval & Search

1. Noah A. Smith
2. Doug Downey
3. Ana Marasovic
4. Sarah Wiegreffe
5. Matthew E. Peters
6. Iz Beltagy

In Natural Language Processing

1. Ana Marasovic
2. Sarah Wiegreffe
3. Noah A. Smith
4. Matthew E. Peters
5. Iz Beltagy
6. Doug Downey
7. Yejin Choi
8. Mohit Bansal

Citations in the corpus

• Few-Shot Self-Rationalization with Natural Language Prompts (2021, background): “On the other hand, a standard approach to generating free-text explanations is to use human-written explanations (Hendricks et al., 2016; Kim et al., 2018; Hancock et al., 2018; Camburu et al., 2018; Ehsan et al., 2018; Liu et al., 2019; Wu and Mooney, 2019; Narang et al., 2020; Tang et al., 2020; M”
• Measuring Association Between Labels and Free-Text Rationales (2020, background): “…common approach to free-text rationalization (Hendricks et al., 2016; Kim et al., 2018; Hancock et al., 2018; Camburu et al., 2018; Ehsan et al., 2018; Liu et al., 2019a; Wu and Mooney, 2019; Narang et al., 2020; Do et al., 2020; Tang et al., 2020), but little is understood about model internals.”