One Small and One Large for Document-level Event Argument Extraction
Document-level Event Argument Extraction (EAE) faces two challenges due to increased input length: 1) difficulty in distinguishing semantic boundaries between events, and 2) interference from redundant information. To address these issues, we propose two methods. The first method introduces the Co a...
Gespeichert in:
Hauptverfasser: | , , , , , |
---|---|
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext bestellen |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
Zusammenfassung: | Document-level Event Argument Extraction (EAE) faces two challenges due to
increased input length: 1) difficulty in distinguishing semantic boundaries
between events, and 2) interference from redundant information. To address
these issues, we propose two methods. The first method introduces the Co and
Structure Event Argument Extraction model (CsEAE) based on Small Language
Models (SLMs). CsEAE includes a co-occurrences-aware module, which integrates
information about all events present in the current input through context
labeling and co-occurrences event prompts extraction. Additionally, CsEAE
includes a structure-aware module that reduces interference from redundant
information by establishing structural relationships between the sentence
containing the trigger and other sentences in the document. The second method
introduces new prompts to transform the extraction task into a generative task
suitable for Large Language Models (LLMs), addressing gaps in EAE performance
using LLMs under Supervised Fine-Tuning (SFT) conditions. We also fine-tuned
multiple datasets to develop an LLM that performs better across most datasets.
Finally, we applied insights from CsEAE to LLMs, achieving further performance
improvements. This suggests that reliable insights validated on SLMs are also
applicable to LLMs. We tested our models on the Rams, WikiEvents, and MLEE
datasets. The CsEAE model achieved improvements of 2.1\%, 2.3\%, and 3.2\% in
the Arg-C F1 metric compared to the baseline, PAIE~\cite{PAIE}. For LLMs, we
demonstrated that their performance on document-level datasets is comparable to
that of SLMs~\footnote{All code is available at
https://github.com/simon-p-j-r/CsEAE}. |
---|---|
DOI: | 10.48550/arxiv.2411.05895 |