activity
20202024
most citedImproving Chinese Story Generation via Awareness of Syntactic Dependencies and Semantics

7 citations · 20 across the 6 of their papers we have counts for

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9 papers · 1 filter

cs.CL2024

SLIDE: A Framework Integrating Small and Large Language Models for Open-Domain Dialogues Evaluation

Kun Zhao, Bohao Yang, Chen Tang +2

The long-standing one-to-many problem of gold standard responses in open-domain dialogue systems presents challenges for automatic evaluation metrics. Though prior works have demon…

cs.CL2024

Emphasising Structured Information: Integrating Abstract Meaning Representation into LLMs for Enhanced Open-Domain Dialogue Evaluation

Bohao Yang, Kun Zhao, Dong Liu +3

Automatic open-domain dialogue evaluation has attracted increasing attention, yet remains challenging due to the complexity of assessing response appropriateness. Traditional evalu…

cs.CL2024

Train & Constrain: Phonologically Informed Tongue-Twister Generation from Topics and Paraphrases

Tyler Loakman, Chen Tang, Chenghua Lin

Previous work in phonologically and phonetically grounded language generation has mainly focused on domains such as puns and poetry. In this article, we present new work on the gen…

cs.CL2023

A Cross-Attention Augmented Model for Event-Triggered Context-Aware Story Generation

Chen Tang, Tyler Loakman, Chenghua Lin

Despite recent advancements, existing story generation systems continue to encounter difficulties in effectively incorporating contextual and event features, which greatly influenc…

cs.CL20232 cited

Improving Medical Dialogue Generation with Abstract Meaning Representations

Bohao Yang, Chen Tang, Chenghua Lin

Medical Dialogue Generation serves a critical role in telemedicine by facilitating the dissemination of medical expertise to patients. Existing studies focus on incorporating textu…

cs.CL2023

Effective Distillation of Table-based Reasoning Ability from LLMs

Bohao Yang, Chen Tang, Kun Zhao +2

Large Language Models (LLMs) have demonstrated remarkable performance across a wide range of natural language processing tasks. However, their enormous parameter size and extremely…