3 citations · 13 across the 19 of their papers we have counts for
19 papers
Reading Between the Frames of Multimodal Drivelology
Yang Wang, Yanan Ma, Yiqi Liu +8
Social media videos often communicate meanings that go beyond their visible actions, captions, or speech. A mundane clip may become humorous, ironic, or satire only through the int…
Exploring Task Performance with Interpretable Models via Sparse Auto-Encoders
Shun Wang, Tyler Loakman, Youbo Lei +5
Large Language Models (LLMs) are traditionally viewed as black-box algorithms, therefore reducing trustworthiness and obscuring potential approaches to increasing performance on do…
DRE: An Effective Dual-Refined Method for Integrating Small and Large Language Models in Open-Domain Dialogue Evaluation
Kun Zhao, Bohao Yang, Chen Tang +4
Large Language Models (LLMs) excel at many tasks but struggle with ambiguous scenarios where multiple valid responses exist, often yielding unreliable results. Conversely, Small La…
Does Table Source Matter? Benchmarking and Improving Multimodal Scientific Table Understanding and Reasoning
Bohao Yang, Yingji Zhang, Dong Liu +2
Recent large language models (LLMs) have advanced table understanding capabilities but rely on converting tables into text sequences. While multimodal large language models (MLLMs)…
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…
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…
The Iron(ic) Melting Pot: Reviewing Human Evaluation in Humour, Irony and Sarcasm Generation
Tyler Loakman, Aaron Maladry, Chenghua Lin
Human evaluation is often considered to be the gold standard method of evaluating a Natural Language Generation system. However, whilst its importance is accepted by the community…
ACL Anthology Helper: A Tool to Retrieve and Manage Literature from ACL Anthology
Chen Tang, Frank Guerin, Chenghua Lin
The ACL Anthology is an online repository that serves as a comprehensive collection of publications in the field of natural language processing (NLP) and computational linguistics…
Enhancing Biomedical Lay Summarisation with External Knowledge Graphs
Tomas Goldsack, Zhihao Zhang, Chen Tang +2
Previous approaches for automatic lay summarisation are exclusively reliant on the source article that, given it is written for a technical audience (e.g., researchers), is unlikel…
Improving Biomedical Abstractive Summarisation with Knowledge Aggregation from Citation Papers
Chen Tang, Shun Wang, Tomas Goldsack +1
Abstracts derived from biomedical literature possess distinct domain-specific characteristics, including specialised writing styles and biomedical terminologies, which necessitate…
Compressing Context to Enhance Inference Efficiency of Large Language Models
Yucheng Li, Bo Dong, Chenghua Lin +1
Large language models (LLMs) achieved remarkable performance across various tasks. However, they face challenges in managing long documents and extended conversations, due to signi…
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…