Papers by Henglin Huang

2 papers
Improving Chinese Story Generation via Awareness of Syntactic Dependencies and Semantics (2022.aacl-short)

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Challenge: Current neural models for Chinese story generation struggle to generate high-quality long text narratives due to ambiguity in syntactically parsing the Chinese language.
Approach: They propose a framework that enhances the feature capturing mechanism by informing the generation model of dependencies between words and additionally augmenting the semantic representation learning through synonym denoising training.
Outcome: The proposed framework outperforms the state-of-the-art Chinese generation models on all evaluation metrics, showing that it enhances dependency and semantic representation learning.
EtriCA: Event-Triggered Context-Aware Story Generation Augmented by Cross Attention (2022.findings-emnlp)

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Challenge: Existing methods for story generation still suffer from problems of relevance and coherence.
Approach: They propose a novel neural generation model which maps contextual and event features to event sequences with a cross-attention mechanism and exploits logical relatedness between events.
Outcome: The proposed model outperforms state-of-the-art models on automatic and human evaluations and shows that it can leverage contextual and event features.

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