Speaker-Oriented Latent Structures for Dialogue-Based Relation Extraction

Published in

arXiv.org(2021)

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TL;DR

This work introduces SOLS, a novel model which can explicitly induce speaker-oriented latent structures for better DiaRE, and learns latent structures to capture the relationships among tokens beyond the utterance boundaries, alleviating the entangled logic issue.

Abstract

Dialogue-based relation extraction (DiaRE) aims to detect the structural information from unstructured utterances in dialogues. Existing relation extraction models may be unsatisfactory under such a conversational setting, due to the entangled logic and information sparsity issues in utterances involving multiple speakers. To this end, we introduce SOLS, a novel model which can explicitly induce speaker-oriented latent structures for better DiaRE. Specifically, we learn latent structures to capture the relationships among tokens beyond the utterance boundaries, alleviating the entangled logic issue. During the learning process, our speaker-specific regularization method progressively highlights speaker-related key clues and erases the irrelevant ones, alleviating the information sparsity issue. Experiments on three public datasets demonstrate the effectiveness of our proposed approach.

Authors

Wei Lu

8 papers

Guoshun Nan

3 papers

Sicong Leng

2 papers

References77 items

Interventional Video Relation Detection

Uncovering Main Causalities for Long-tailed Information Extraction

PRGC: Potential Relation and Global Correspondence Based Joint Relational Triple Extraction

Document-level Relation Extraction as Semantic Segmentation

AliCG: Fine-grained and Evolvable Conceptual Graph Construction for Semantic Search at Alibaba

Speaker-Oriented Latent Structures for Dialogue-Based Relation Extraction

Published in

arXiv.org(2021)

External Links:

Generate Graph

TL;DR

Abstract

Authors

Wei Lu

8 papers

Guoshun Nan

3 papers

Sicong Leng

2 papers

References77 items

Interventional Video Relation Detection

Uncovering Main Causalities for Long-tailed Information Extraction

PRGC: Potential Relation and Global Correspondence Based Joint Relational Triple Extraction

Document-level Relation Extraction as Semantic Segmentation

AliCG: Fine-grained and Evolvable Conceptual Graph Construction for Semantic Search at Alibaba

Guoqing Luo

1 papers

Interventional Video Grounding with Dual Contrastive Learning

Semantic Representation for Dialogue Modeling

TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance

Video Corpus Moment Retrieval with Contrastive Learning

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S3: I'll take that dad. S1: It seems your daughter and Richard are

The latent structures are then fed to SOLS encoder, which is a graph convolution network (GCN) (Kipf and Welling, 2017) for information

To infer the relation between Speaker 3 (S3) and Speaker 4 (S4

Field of Study

Computer Science

Journal Information

Name

ArXiv

Volume

abs/2005.00687

Venue Information

Name

arXiv.org

Type

URL

https://arxiv.org

Alternate Names

ArXiv