5 research outputs found
One for All: Neural Joint Modeling of Entities and Events
The previous work for event extraction has mainly focused on the predictions
for event triggers and argument roles, treating entity mentions as being
provided by human annotators. This is unrealistic as entity mentions are
usually predicted by some existing toolkits whose errors might be propagated to
the event trigger and argument role recognition. Few of the recent work has
addressed this problem by jointly predicting entity mentions, event triggers
and arguments. However, such work is limited to using discrete engineering
features to represent contextual information for the individual tasks and their
interactions. In this work, we propose a novel model to jointly perform
predictions for entity mentions, event triggers and arguments based on the
shared hidden representations from deep learning. The experiments demonstrate
the benefits of the proposed method, leading to the state-of-the-art
performance for event extraction.Comment: Accepted at The Thirty-Third AAAI Conference on Artificial
Intelligence (AAAI-19) (Honolulu, Hawaii, USA
GATE: Graph Attention Transformer Encoder for Cross-lingual Relation and Event Extraction
Recent progress in cross-lingual relation and event extraction use graph
convolutional networks (GCNs) with universal dependency parses to learn
language-agnostic sentence representations such that models trained on one
language can be applied to other languages. However, GCNs struggle to model
words with long-range dependencies or are not directly connected in the
dependency tree. To address these challenges, we propose to utilize the
self-attention mechanism where we explicitly fuse structural information to
learn the dependencies between words with different syntactic distances. We
introduce GATE, a {\bf G}raph {\bf A}ttention {\bf T}ransformer {\bf E}ncoder,
and test its cross-lingual transferability on relation and event extraction
tasks. We perform experiments on the ACE05 dataset that includes three
typologically different languages: English, Chinese, and Arabic. The evaluation
results show that GATE outperforms three recently proposed methods by a large
margin. Our detailed analysis reveals that due to the reliance on syntactic
dependencies, GATE produces robust representations that facilitate transfer
across languages.Comment: AAAI 202