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

Challenge:	EE is a key requirement for machine learning in many domains, e.g., legal, medical, finance.
Approach:	They propose an interpretable approach for event extraction that jointly trains a classifier and a rule decoder for event processing.
Outcome:	The proposed approach can be used for semi-supervised learning and its performance improves when trained on automatically-labeled data generated by a rule-based system.

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Overview

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Approach

Outcome

A Hybrid Detection and Generation Framework with Separate Encoders for Event Extraction (2023.eacl-main)

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Ge Shi, Yunyue Su, Yongliang Ma, Ming Zhou

Language Models

Challenge:	Recent work on event extraction tasks has been based on classification-based methods . a new generation-based method is being developed to extract event triggers and event arguments from plain text.
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Text-to-Text Extraction and Verbalization of Biomedical Event Graphs (2022.coling-1)

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Giacomo Frisoni, Gianluca Moro, Lorenzo Balzani

Information Extraction & Text Mining

Challenge:	Biomedical events represent complex, graphical, and semantically rich interactions expressed in the scientific literature.
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From Examples to Rules: Neural Guided Rule Synthesis for Information Extraction (2022.lrec-1)

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Robert Vacareanu, Marco A. Valenzuela-Escárcega, George Caique Gouveia Barbosa, Rebecca Sharp, Gustave Hahn-Powell, Mihai Surdeanu

Information Extraction & Text Mining

Challenge:	a "deep learning tsunami" has brought tremendous improvements in performance to most NLP applications.
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Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction (2021.acl-long)

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Yaojie Lu, Hongyu Lin, Jin Xu, Xianpei Han, Jialong Tang, Annan Li, Le Sun, Meng Liao, Shaoyi Chen

Information Extraction & Text Mining

Challenge:	Existing methods to extract event records from text decompose complex structure prediction task into multiple subtasks.
Approach:	They propose a sequence-to-structure generation paradigm that can extract events from text . they propose unified event extraction, constrained decoding algorithm and curriculum learning algorithm .
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Inseq: An Interpretability Toolkit for Sequence Generation Models (2023.acl-demo)

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Gabriele Sarti, Nils Feldhus, Ludwig Sickert, Oskar van der Wal

Semantic Text Processing

Challenge:	Recent studies focused on classification tasks while largely overlooking generation settings due to a lack of dedicated tools.
Approach:	They propose to use Inseq to democratize access to interpretability analyses of sequence generation models by enabling intuitive extraction of models’ internal information and feature importance scores for popular decoder-only and encoder-decoder Transformers architectures.
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Query and Extract: Refining Event Extraction as Type-oriented Binary Decoding (2022.findings-acl)

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Sijia Wang, Mo Yu, Shiyu Chang, Lichao Sun, Lifu Huang

Information Extraction & Text Mining

Challenge:	Existing approaches to event extraction are limited to a set of pre-defined types.
Approach:	They propose a natural language query framework that uses event types and argument roles to extract candidate triggers and arguments from input text.
Outcome:	The proposed framework outperforms existing methods on zero-shot event extraction.

A Joint Neural Model for Information Extraction with Global Features (2020.acl-main)

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Ying Lin, Heng Ji, Fei Huang, Lingfei Wu

Information Extraction & Text Mining

Challenge:	Existing joint neural models for Information Extraction use local task-specific classifiers to predict labels for individual instances.
Approach:	They propose a joint neural framework that extracts the optimal IE result as a graph from an input sentence.
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TRACE: Training and Inference-Time Interpretability Analysis for Language Models (2025.emnlp-demos)

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Nura Aljaafari, Danilo Carvalho, Andre Freitas

Explainability & Interpretability in NLP

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Approach:	They propose a modular toolkit for training and inference-time interpretability analysis of transformer models.
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TextEE: Benchmark, Reevaluation, Reflections, and Future Challenges in Event Extraction (2024.findings-acl)

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Kuan-Hao Huang, I-Hung Hsu, Tanmay Parekh, Zhiyu Xie, Zixuan Zhang, Prem Natarajan, Kai-Wei Chang, Nanyun Peng, Heng Ji

Information Extraction & Text Mining

Challenge:	Recent studies suggest that event extraction evaluations may not accurately reflect the true performance.
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ESTER: A Machine Reading Comprehension Dataset for Reasoning about Event Semantic Relations (2021.emnlp-main)

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Rujun Han, I-Hung Hsu, Jiao Sun, Julia Baylon, Qiang Ning, Dan Roth, Nanyun Peng

Reasoning

Challenge:	Recent event-centric reading comprehension datasets focus mostly on event arguments or temporal relations.
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