TLAG: An Informative Trigger and Label-Aware Knowledge Guided Model for Dialogue-based Relation Extraction

An, Hao; Chen, Dongsheng; Xu, Wenli; Zhu, Zhiliang; Zou, Yuexian

doi:10.1109/cscwd57460.2023.10152766

2023 26th International Conference on Computer Supported Cooperative Work in Design (CSCWD) 2023

DOI: 10.1109/cscwd57460.2023.10152766

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TLAG: An Informative Trigger and Label-Aware Knowledge Guided Model for Dialogue-based Relation Extraction

Hao An

Dongsheng Chen

Wenli Xu

et al.

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Prompt Language Learner with Trigger Generation for Dialogue Relation Extraction

Kim,

Son

et al. 2023

Applied Sciences

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Dialogue relation extraction identifies semantic relations between entity pairs in dialogues. This research explores a methodology harnessing the potential of prompt-based fine-tuning paired with a trigger-generation approach. Capitalizing on the intrinsic knowledge of pre-trained language models, this strategy employs triggers that underline the relation between entities decisively. In particular, diverging from the conventional extractive methods seen in earlier research, our study leans towards a generative manner for trigger generation. The dialogue-based relation extraction (DialogeRE) benchmark dataset features multi-utterance environments of colloquial speech by multiple speakers, making it critical to capture meaningful clues for inferring relational facts. In the benchmark, empirical results reveal significant performance boosts in few-shot scenarios, where the availability of examples is notably limited. Nevertheless, the scarcity of ground-truth triggers for training hints at potential further refinements in the trigger-generation module, especially when ample examples are present. When evaluating the challenges of dialogue relation extraction, combining prompt-based learning with trigger generation offers pronounced improvements in both full-shot and few-shot scenarios. Specifically, integrating a meticulously crafted manual initialization method with the prompt-based model—considering prior distributional insights and relation class semantics—substantially surpasses the baseline. However, further advancements in trigger generation are warranted, especially in data-abundant contexts, to maximize performance enhancements.

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Prompt Language Learner with Trigger Generation for Dialogue Relation Extraction

Kim,

Son

et al. 2023

Applied Sciences

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show abstract

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TLAG: An Informative Trigger and Label-Aware Knowledge Guided Model for Dialogue-based Relation Extraction

Cited by 1 publication

References 22 publications

Prompt Language Learner with Trigger Generation for Dialogue Relation Extraction

Prompt Language Learner with Trigger Generation for Dialogue Relation Extraction

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