Reinforcement Learning-based N-ary Cross-Sentence Relation Extraction
- URL: http://arxiv.org/abs/2009.12683v1
- Date: Sat, 26 Sep 2020 20:39:55 GMT
- Title: Reinforcement Learning-based N-ary Cross-Sentence Relation Extraction
- Authors: Chenhan Yuan, Ryan Rossi, Andrew Katz, and Hoda Eldardiry
- Abstract summary: Models of n-ary cross sentence relation extraction based on distant supervision assume that consecutive sentences mentioning n entities describe the relation of these n entities.
On the other hand, some non-consecutive sentences also describe one relation and these sentences cannot be labeled under this assumption.
We propose a novel sentence distribution estimator model to address the first problem.
- Score: 3.342376225738321
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The models of n-ary cross sentence relation extraction based on distant
supervision assume that consecutive sentences mentioning n entities describe
the relation of these n entities. However, on one hand, this assumption
introduces noisy labeled data and harms the models' performance. On the other
hand, some non-consecutive sentences also describe one relation and these
sentences cannot be labeled under this assumption. In this paper, we relax this
strong assumption by a weaker distant supervision assumption to address the
second issue and propose a novel sentence distribution estimator model to
address the first problem. This estimator selects correctly labeled sentences
to alleviate the effect of noisy data is a two-level agent reinforcement
learning model. In addition, a novel universal relation extractor with a hybrid
approach of attention mechanism and PCNN is proposed such that it can be
deployed in any tasks, including consecutive and nonconsecutive sentences.
Experiments demonstrate that the proposed model can reduce the impact of noisy
data and achieve better performance on general n-ary cross sentence relation
extraction task compared to baseline models.
Related papers
- Entity or Relation Embeddings? An Analysis of Encoding Strategies for Relation Extraction [19.019881161010474]
Relation extraction is essentially a text classification problem, which can be tackled by fine-tuning a pre-trained language model (LM)
Existing approaches therefore solve the problem in an indirect way: they fine-tune an LM to learn embeddings of the head and tail entities, and then predict the relationship from these entity embeddings.
Our hypothesis in this paper is that relation extraction models can be improved by capturing relationships in a more direct way.
arXiv Detail & Related papers (2023-12-18T09:58:19Z) - Siamese Representation Learning for Unsupervised Relation Extraction [5.776369192706107]
Unsupervised relation extraction (URE) aims at discovering underlying relations between named entity pairs from open-domain plain text.
Existing URE models utilizing contrastive learning, which attract positive samples and repulse negative samples to promote better separation, have got decent effect.
We propose Siamese Representation Learning for Unsupervised Relation Extraction -- a novel framework to simply leverage positive pairs to representation learning.
arXiv Detail & Related papers (2023-10-01T02:57:43Z) - Advancing Counterfactual Inference through Nonlinear Quantile Regression [77.28323341329461]
We propose a framework for efficient and effective counterfactual inference implemented with neural networks.
The proposed approach enhances the capacity to generalize estimated counterfactual outcomes to unseen data.
Empirical results conducted on multiple datasets offer compelling support for our theoretical assertions.
arXiv Detail & Related papers (2023-06-09T08:30:51Z) - HiURE: Hierarchical Exemplar Contrastive Learning for Unsupervised
Relation Extraction [60.80849503639896]
Unsupervised relation extraction aims to extract the relationship between entities from natural language sentences without prior information on relational scope or distribution.
We propose a novel contrastive learning framework named HiURE, which has the capability to derive hierarchical signals from relational feature space using cross hierarchy attention.
Experimental results on two public datasets demonstrate the advanced effectiveness and robustness of HiURE on unsupervised relation extraction when compared with state-of-the-art models.
arXiv Detail & Related papers (2022-05-04T17:56:48Z) - SAIS: Supervising and Augmenting Intermediate Steps for Document-Level
Relation Extraction [51.27558374091491]
We propose to explicitly teach the model to capture relevant contexts and entity types by supervising and augmenting intermediate steps (SAIS) for relation extraction.
Based on a broad spectrum of carefully designed tasks, our proposed SAIS method not only extracts relations of better quality due to more effective supervision, but also retrieves the corresponding supporting evidence more accurately.
arXiv Detail & Related papers (2021-09-24T17:37:35Z) - Avoiding Inference Heuristics in Few-shot Prompt-based Finetuning [57.4036085386653]
We show that prompt-based models for sentence pair classification tasks still suffer from a common pitfall of adopting inferences based on lexical overlap.
We then show that adding a regularization that preserves pretraining weights is effective in mitigating this destructive tendency of few-shot finetuning.
arXiv Detail & Related papers (2021-09-09T10:10:29Z) - Improving Distantly Supervised Relation Extraction with Self-Ensemble
Noise Filtering [17.45521023572853]
We propose a self-ensemble filtering mechanism to filter out noisy samples during the training process.
Our experiments with multiple state-of-the-art relation extraction models show that our proposed filtering mechanism improves the robustness of the models and increases their F1 scores.
arXiv Detail & Related papers (2021-08-22T11:23:36Z) - Sentence Similarity Based on Contexts [31.135984064747607]
The proposed framework is based on the core idea that the meaning of a sentence should be defined by its contexts.
It is able to generate high-quality, large-scale dataset with semantic similarity scores between two sentences in an unsupervised manner.
arXiv Detail & Related papers (2021-05-17T06:03:56Z) - Understanding Neural Abstractive Summarization Models via Uncertainty [54.37665950633147]
seq2seq abstractive summarization models generate text in a free-form manner.
We study the entropy, or uncertainty, of the model's token-level predictions.
We show that uncertainty is a useful perspective for analyzing summarization and text generation models more broadly.
arXiv Detail & Related papers (2020-10-15T16:57:27Z) - Accounting for Unobserved Confounding in Domain Generalization [107.0464488046289]
This paper investigates the problem of learning robust, generalizable prediction models from a combination of datasets.
Part of the challenge of learning robust models lies in the influence of unobserved confounders.
We demonstrate the empirical performance of our approach on healthcare data from different modalities.
arXiv Detail & Related papers (2020-07-21T08:18:06Z)
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.