GDPNet: Refining Latent Multi-View Graph for Relation Extraction

Xue, Fuzhao; Sun, Aixin; Zhang, Hao; Chng, Eng Siong

doi:10.1609/aaai.v35i16.17670

Cited by 42 publications

(17 citation statements)

References 23 publications

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“…Unlike previous work, we focused on extracting relations from dialogues, which are texts with high pronoun frequencies and low information density. (Yu et al, 2020;Xue et al, 2021) were among the early works on dialogue-based RE. Yu et al (2020) introduced several dialogue-based RE approaches with the DialogRE dataset.…”

Section: Dialogue-based Relation Extractionmentioning

confidence: 99%

Graph Based Network with Contextualized Representations of Turns in Dialogue

Lee¹,

Choi²

2021

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

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Dialogue-based relation extraction (RE) aimsto extract relation(s) between two arguments that appear in a dialogue. Because dialogues have the characteristics of high personal pronoun occurrences and low information density, and since most relational facts in dialogues are not supported by any single sentence, dialogue-based relation extraction requires a comprehensive understanding of dialogue. In this paper, we propose the TUrn COntext awaRE Graph Convolutional Network (TUCORE-GCN) modeled by paying attention to the way people understand dialogues. In addition, we propose a novel approach which treats the task of emotion recognition in conversations (ERC) as a dialoguebased RE. Experiments on a dialogue-based RE dataset and three ERC datasets demonstrate that our model is very effective in various dialogue-based natural language understanding tasks.In these experiments, TUCORE-GCN outperforms the state-of-theart models on most of the benchmark datasets. Our code is available at https://github. com/BlackNoodle/TUCORE-GCN.

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Section: Dialogue-based Relation Extractionmentioning

confidence: 99%

Graph Based Network with Contextualized Representations of Turns in Dialogue

Lee¹,

Choi²

2021

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

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“…Christopoulou et al [23] constructed a heterogeneous graph with three types of nodes and edges, iteratively modeling the longrange semantic dependency among entity pairs over a document. Xue et al [24] generated a latent multi-view graph using a Gaussian graph generator to capture the possible relationships among tokens. Li et al [7] devised a heterogeneous affinity graph inference network with noise suppression mechanism to build the long-distance reasoning chain in document-level RE.…”

Section: Related Workmentioning

confidence: 99%

Denoising Graph Inference Network for Document-Level Relation Extraction

Wang

Qin

Duan

et al. 2023

Big Data Min. Anal.

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Relation Extraction (RE) is to obtain a predefined relation type of two entities mentioned in a piece of text, e.g., a sentence-level or a document-level text. Most existing studies suffer from the noise in the text, and necessary pruning is of great importance. The conventional sentence-level RE task addresses this issue by a denoising method using the shortest dependency path to build a long-range semantic dependency between entity pairs. However, this kind of denoising method is scarce in document-level RE. In this work, we explicitly model a denoised document-level graph based on linguistic knowledge to capture various long-range semantic dependencies among entities. We first formalize a Syntactic Dependency Tree forest (SDT-forest) by introducing the syntax and discourse dependency relation. Then, the Steiner tree algorithm extracts a mention-level denoised graph, Steiner Graph (SG), removing linguistically irrelevant words from the SDT-forest. We then devise a slide residual attention to highlight word-level evidence on text and SG. Finally, the classification is established on the SG to infer the relations of entity pairs. We conduct extensive experiments on three public datasets. The results evidence that our method is beneficial to establish long-range semantic dependency and can improve the classification performance with longer texts.

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“…There are also researches that extract information from natural conversations. Some of them extract relationships among persons on a domainspecific dataset Xue et al, 2021;Long et al, 2021), while they focus on relation extraction not response generation. Others construct conversational graph from natural conversations to improve response generation (Xu et al, 2020b;Zou et al, 2021).…”

Section: Case Studymentioning

confidence: 99%

Enhancing Dialogue Generation with Conversational Concept Flows

Li,

Jiang,

et al. 2023

Findings of the Association for Computational Linguistics: EACL 2023

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Human conversations contain natural and reasonable topic shifts, reflected as the concept flows across utterances. Previous researches prove that explicitly modeling concept flows with a large commonsense knowledge graph effectively improves response quality. However, we argue that there exists a gap between the knowledge graph and the conversation. The knowledge graph has limited commonsense knowledge and ignores the characteristics of natural conversations. Thus, many concepts and relations in conversations are not included. To bridge this gap, we propose to enhance dialogue generation with conversational concept flows. Specifically, we extract abundant concepts and relations from natural conversations and build a new conversation-aware knowledge graph. In addition, we design a novel relationaware graph encoder to capture the concept flows guided by the knowledge graph. Experimental results on the large-scale Reddit conversation dataset indicate that our method performs better than strong baselines, and further analysis verifies the effectiveness of each component.

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GDPNet: Refining Latent Multi-View Graph for Relation Extraction

Cited by 42 publications

References 23 publications

Graph Based Network with Contextualized Representations of Turns in Dialogue

Graph Based Network with Contextualized Representations of Turns in Dialogue

Denoising Graph Inference Network for Document-Level Relation Extraction

Enhancing Dialogue Generation with Conversational Concept Flows

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