52 research outputs found
Consistency Guided Knowledge Retrieval and Denoising in LLMs for Zero-shot Document-level Relation Triplet Extraction
Document-level Relation Triplet Extraction (DocRTE) is a fundamental task in
information systems that aims to simultaneously extract entities with semantic
relations from a document. Existing methods heavily rely on a substantial
amount of fully labeled data. However, collecting and annotating data for newly
emerging relations is time-consuming and labor-intensive. Recent advanced Large
Language Models (LLMs), such as ChatGPT and LLaMA, exhibit impressive long-text
generation capabilities, inspiring us to explore an alternative approach for
obtaining auto-labeled documents with new relations. In this paper, we propose
a Zero-shot Document-level Relation Triplet Extraction (ZeroDocRTE) framework,
which generates labeled data by retrieval and denoising knowledge from LLMs,
called GenRDK. Specifically, we propose a chain-of-retrieval prompt to guide
ChatGPT to generate labeled long-text data step by step. To improve the quality
of synthetic data, we propose a denoising strategy based on the consistency of
cross-document knowledge. Leveraging our denoised synthetic data, we proceed to
fine-tune the LLaMA2-13B-Chat for extracting document-level relation triplets.
We perform experiments for both zero-shot document-level relation and triplet
extraction on two public datasets. The experimental results illustrate that our
GenRDK framework outperforms strong baselines.Comment: Accepted by WWW 202
Denoising Relation Extraction from Document-level Distant Supervision
Distant supervision (DS) has been widely used to generate auto-labeled data
for sentence-level relation extraction (RE), which improves RE performance.
However, the existing success of DS cannot be directly transferred to the more
challenging document-level relation extraction (DocRE), since the inherent
noise in DS may be even multiplied in document level and significantly harm the
performance of RE. To address this challenge, we propose a novel pre-trained
model for DocRE, which denoises the document-level DS data via multiple
pre-training tasks. Experimental results on the large-scale DocRE benchmark
show that our model can capture useful information from noisy DS data and
achieve promising results.Comment: EMNLP 2020 short pape
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