3 citations · 13 across the 19 of their papers we have counts for
8 papers · 1 filter
CAST: Corpus-Aware Self-similarity Enhanced Topic modelling
Yanan Ma, Chenghao Xiao, Chenhan Yuan +4
Topic modelling is a pivotal unsupervised machine learning technique for extracting valuable insights from large document collections. Existing neural topic modelling methods often…
BioMNER: A Dataset for Biomedical Method Entity Recognition
Chen Tang, Bohao Yang, Kun Zhao +4
Named entity recognition (NER) stands as a fundamental and pivotal task within the realm of Natural Language Processing. Particularly within the domain of Biomedical Method NER, th…
Crafting Customisable Characters with LLMs: A Persona-Driven Role-Playing Agent Framework
Bohao Yang, Dong Liu, Chenghao Xiao +6
Large Language Models (LLMs) demonstrate remarkable ability to comprehend instructions and generate human-like text, enabling sophisticated agent simulation beyond basic behavior r…
X-ray Made Simple: Lay Radiology Report Generation and Robust Evaluation
Kun Zhao, Chenghao Xiao, Sixing Yan +5
Radiology Report Generation (RRG) has advanced considerably with the development of multimodal generative models. Despite the progress, the field still faces significant challenges…
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…
ReproHum #0087-01: Human Evaluation Reproduction Report for Generating Fact Checking Explanations
Tyler Loakman, Chenghua Lin
This paper presents a partial reproduction of Generating Fact Checking Explanations by Anatanasova et al (2020) as part of the ReproHum element of the ReproNLP shared task to repro…
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…
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…