United Arab Emirates

Event extraction (EE) is crucial to downstream tasks such as new aggregation and event knowledge graph construction. Most existing EE datasets manually define fixed event types and design specific schema for each of them, failing to cover diverse events emerging from the online text. Moreover, news titles, an important source of event mentions, have not gained enough attention in current EE research. In this paper, we present Title2Event, a large-scale sentence-level dataset benchmarking Open Event Extraction without restricting event types. Title2Event contains more than 42,000 news titles in 34 topics collected from Chinese web pages. To the best of our knowledge, it is currently the largest manually annotated Chinese dataset for open event extraction. We further conduct experiments on Title2Event with different models and show that the characteristics of titles make it challenging for event extraction, addressing the significance of advanced study on this problem. The dataset and baseline codes are available at https://open-event-hub.github.io/title2event.

EMNLP 2022

Title2Event: Benchmarking Open Event Extraction with a Large-scale Chinese Title Dataset

news titles

chinese dataset

event extraction

poster

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EMNLP 2022 will take place in Abu Dhabi from December 7th to December 11th, 2022. And it will be held in hybrid mode, both online and offline.

General pre-trained language models (PLMs), such as BERT, have achieved remarkable performance on various NLP tasks. Recently, domain-specific PLMs have been proposed to boost the task performance of specific domains (e.g., biomedical and computer science) by continuing to pre-train general PLMs with domain-specific corpora. However, this domain-adaptive pre-training (DAPT \cite{DBLP:conf/acl/GururanganMSLBD20}) tends to forget the previous general knowledge acquired by general PLMs, which leads to a \emph{catastrophic forgetting} phenomenon and sub-optimal performance. To alleviate this problem, we propose a new framework of \textbf{M}emory-\textbf{A}ugmented \textbf{P}re-trained Language Model (\textbf{MAP}), which augments the domain-specific PLM by a memory built from the frozen general PLM without losing the general knowledge. Specifically, we propose a new memory-augmented layer, and based on it, different augmentation strategies are explored to build memory and fusion memory into domain-specific PLM. We demonstrate the effectiveness of MAP on different domains (biomedical and computer science publications, news, and reviews) and different kinds (text classification, QA, NER) of tasks, and the extensive results show that the proposed MAP can achieve SOTA results on these tasks.

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Eeny, meeny, miny, moe. How to choose data for morphological inflection.

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Title2Event: Benchmarking Open Event Extraction with a Large-scale Chinese Title Dataset

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G-MAP: General Memory-Augmented Pre-trained Language Model for Domain Tasks

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