A Sequence Tagging based Framework for Few-Shot Relation Extraction

• URL: http://arxiv.org/abs/2208.08053v1
• Date: Wed, 17 Aug 2022 03:54:22 GMT
• Title: A Sequence Tagging based Framework for Few-Shot Relation Extraction
• Authors: Xukun Luo and Ping Wang
• Abstract summary: Relation Extraction (RE) refers to extracting the relation triples in the input text. We put forward the definition of the few-shot RE task based on the sequence tagging joint extraction approaches, and propose a few-shot RE framework for the task.
• Score: 5.536010796119412
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Relation Extraction (RE) refers to extracting the relation triples in the input text. Existing neural work based systems for RE rely heavily on manually labeled training data, but there are still a lot of domains where sufficient labeled data does not exist. Inspired by the distance-based few-shot named entity recognition methods, we put forward the definition of the few-shot RE task based on the sequence tagging joint extraction approaches, and propose a few-shot RE framework for the task. Besides, we apply two actual sequence tagging models to our framework (called Few-shot TPLinker and Few-shot BiTT), and achieves solid results on two few-shot RE tasks constructed from a public dataset.

Related papers

• AutoRE: Document-Level Relation Extraction with Large Language Models [27.426703757501507]
  We introduce AutoRE, an end-to-end DocRE model that adopts a novel RE extraction paradigm named RHF (Relation-Head-Facts) Unlike existing approaches, AutoRE does not rely on the assumption of known relation options, making it more reflective of real-world scenarios. Our experiments on the RE-DocRED dataset showcase AutoRE's best performance, achieving state-of-the-art results.
  arXiv  Detail & Related papers  (2024-03-21T23:48:21Z)
• BitCoin: Bidirectional Tagging and Supervised Contrastive Learning based Joint Relational Triple Extraction Framework [16.930809038479666]
  We propose BitCoin, an innovative Bidirectional tagging and supervised Contrastive learning based joint relational triple extraction framework. Specifically, we design a supervised contrastive learning method that considers multiple positives per anchor rather than restricting it to just one positive. Our framework implements taggers in two directions, enabling triples extraction from subject to object and object to subject.
  arXiv  Detail & Related papers  (2023-09-21T07:55:54Z)
• Silver Syntax Pre-training for Cross-Domain Relation Extraction [20.603482820770356]
  Relation Extraction (RE) remains a challenging task, especially when considering realistic out-of-domain evaluations. obtaining high-quality (manually annotated) data is extremely expensive and cannot realistically be repeated for each new domain. An intermediate training step on data from related tasks has shown to be beneficial across many NLP tasks.However, this setup still requires supplementary annotated data, which is often not available. In this paper, we investigate intermediate pre-training specifically for RE. We exploit the affinity between syntactic structure and semantic RE, and identify the syntactic relations closely related to RE by being on the shortest dependency path between two entities
  arXiv  Detail & Related papers  (2023-05-18T14:49:19Z)
• Pic2Word: Mapping Pictures to Words for Zero-shot Composed Image Retrieval [84.11127588805138]
  Composed Image Retrieval (CIR) combines a query image with text to describe their intended target. Existing methods rely on supervised learning of CIR models using labeled triplets consisting of the query image, text specification, and the target image. We propose Zero-Shot Composed Image Retrieval (ZS-CIR), whose goal is to build a CIR model without requiring labeled triplets for training.
  arXiv  Detail & Related papers  (2023-02-06T19:40:04Z)
• ReSel: N-ary Relation Extraction from Scientific Text and Tables by Learning to Retrieve and Select [53.071352033539526]
  We study the problem of extracting N-ary relations from scientific articles. Our proposed method ReSel decomposes this task into a two-stage procedure. Our experiments on three scientific information extraction datasets show that ReSel outperforms state-of-the-art baselines significantly.
  arXiv  Detail & Related papers  (2022-10-26T02:28:02Z)
• Towards Realistic Low-resource Relation Extraction: A Benchmark with Empirical Baseline Study [51.33182775762785]
  This paper presents an empirical study to build relation extraction systems in low-resource settings. We investigate three schemes to evaluate the performance in low-resource settings: (i) different types of prompt-based methods with few-shot labeled data; (ii) diverse balancing methods to address the long-tailed distribution issue; and (iii) data augmentation technologies and self-training to generate more labeled in-domain data.
  arXiv  Detail & Related papers  (2022-10-19T15:46:37Z)
• Few-shot Relational Reasoning via Connection Subgraph Pretraining [81.30830261527231]
  Connection Subgraph Reasoner (CSR) can make predictions for the target few-shot task directly without the need for pre-training. Our framework can already perform competitively to existing methods on target few-shot tasks.
  arXiv  Detail & Related papers  (2022-10-13T04:35:14Z)
• Pre-training to Match for Unified Low-shot Relation Extraction [37.625078897220305]
  Low-shot relation extraction aims to recognize novel relations with very few or even no samples. Few-shot and zero-shot RE are two representative low-shot RE tasks. We propose Multi-Choice Matching Networks to unify low-shot relation extraction.
  arXiv  Detail & Related papers  (2022-03-23T08:43:52Z)
• Automatically Generating Counterfactuals for Relation Exaction [18.740447044960796]
  relation extraction (RE) is a fundamental task in natural language processing. Current deep neural models have achieved high accuracy but are easily affected by spurious correlations. We develop a novel approach to derive contextual counterfactuals for entities.
  arXiv  Detail & Related papers  (2022-02-22T04:46:10Z)
• D-REX: Dialogue Relation Extraction with Explanations [65.3862263565638]
  This work focuses on extracting explanations that indicate that a relation exists while using only partially labeled data. We propose our model-agnostic framework, D-REX, a policy-guided semi-supervised algorithm that explains and ranks relations. We find that about 90% of the time, human annotators prefer D-REX's explanations over a strong BERT-based joint relation extraction and explanation model.
  arXiv  Detail & Related papers  (2021-09-10T22:30:48Z)
• Few-shot Slot Tagging with Collapsed Dependency Transfer and Label-enhanced Task-adaptive Projection Network [61.94394163309688]
  We propose a Label-enhanced Task-Adaptive Projection Network (L-TapNet) based on the state-of-the-art few-shot classification model -- TapNet. Experimental results show that our model significantly outperforms the strongest few-shot learning baseline by 14.64 F1 scores in the one-shot setting.
  arXiv  Detail & Related papers  (2020-06-10T07:50:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.