23 citations · 157 across the 46 of their papers we have counts for
46 papers
Overview of the BioLaySumm 2023 Shared Task on Lay Summarization of Biomedical Research Articles
Tomas Goldsack, Zheheng Luo, Qianqian Xie +4
This paper presents the results of the shared task on Lay Summarisation of Biomedical Research Articles (BioLaySumm), hosted at the BioNLP Workshop at ACL 2023. The goal of this sh…
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…
Enhancing Dialogue Generation via Dynamic Graph Knowledge Aggregation
Chen Tang, Hongbo Zhang, Tyler Loakman +2
Incorporating external graph knowledge into neural chatbot models has been proven effective for enhancing dialogue generation. However, in conventional graph neural networks (GNNs)…
Evaluating Open-Domain Dialogues in Latent Space with Next Sentence Prediction and Mutual Information
Kun Zhao, Bohao Yang, Chenghua Lin +3
The long-standing one-to-many issue of the open-domain dialogues poses significant challenges for automatic evaluation methods, i.e., there may be multiple suitable responses which…
TwistList: Resources and Baselines for Tongue Twister Generation
Tyler Loakman, Chen Tang, Chenghua Lin
Previous work in phonetically-grounded language generation has mainly focused on domains such as lyrics and poetry. In this paper, we present work on the generation of tongue twist…
LyricWhiz: Robust Multilingual Zero-shot Lyrics Transcription by Whispering to ChatGPT
Le Zhuo, Ruibin Yuan, Jiahao Pan +10
We introduce LyricWhiz, a robust, multilingual, and zero-shot automatic lyrics transcription method achieving state-of-the-art performance on various lyrics transcription datasets,…
Metaphor Detection via Explicit Basic Meanings Modelling
Yucheng Li, Shun Wang, Chenghua Lin +1
One noticeable trend in metaphor detection is the embrace of linguistic theories such as the metaphor identification procedure (MIP) for model architecture design. While MIP clearl…
Interactive Natural Language Processing
Zekun Wang, Ge Zhang, Kexin Yang +19
Interactive Natural Language Processing (iNLP) has emerged as a novel paradigm within the field of NLP, aimed at addressing limitations in existing frameworks while aligning with t…
DisCo: Distilled Student Models Co-training for Semi-supervised Text Mining
Weifeng Jiang, Qianren Mao, Chenghua Lin +4
Many text mining models are constructed by fine-tuning a large deep pre-trained language model (PLM) in downstream tasks. However, a significant challenge nowadays is maintaining p…
CADGE: Context-Aware Dialogue Generation Enhanced with Graph-Structured Knowledge Aggregation
Hongbo Zhang, Chen Tang, Tyler Loakman +3
Commonsense knowledge is crucial to many natural language processing tasks. Existing works usually incorporate graph knowledge with conventional graph neural networks (GNNs), resul…
Chinese Open Instruction Generalist: A Preliminary Release
Ge Zhang, Yemin Shi, Ruibo Liu +9
Instruction tuning is widely recognized as a key technique for building generalist language models, which has attracted the attention of researchers and the public with the release…
Requirement Formalisation using Natural Language Processing and Machine Learning: A Systematic Review
Shekoufeh Kolahdouz-Rahimi, Kevin Lano, Chenghua Lin
Improvement of software development methodologies attracts developers to automatic Requirement Formalisation (RF) in the Requirement Engineering (RE) field. The potential advantage…
Metaphor Detection with Effective Context Denoising
Shun Wang, Yucheng Li, Chenghua Lin +2
We propose a novel RoBERTa-based model, RoPPT, which introduces a target-oriented parse tree structure in metaphor detection. Compared to existing models, RoPPT focuses on semantic…
FrameBERT: Conceptual Metaphor Detection with Frame Embedding Learning
Yucheng Li, Shun Wang, Chenghua Lin +2
In this paper, we propose FrameBERT, a RoBERTa-based model that can explicitly learn and incorporate FrameNet Embeddings for concept-level metaphor detection. FrameBERT not only ac…
The Secret of Metaphor on Expressing Stronger Emotion
Yucheng Li, Frank Guerin, Chenghua Lin
Metaphors are proven to have stronger emotional impact than literal expressions. Although this conclusion is shown to be promising in benefiting various NLP applications, the reaso…
CORGI-PM: A Chinese Corpus For Gender Bias Probing and Mitigation
Ge Zhang, Yizhi Li, Yaoyao Wu +5
As natural language processing (NLP) for gender bias becomes a significant interdisciplinary topic, the prevalent data-driven techniques such as large-scale language models suffer…
Routine Outcome Monitoring in Psychotherapy Treatment using Sentiment-Topic Modelling Approach
Noor Fazilla Abd Yusof, Chenghua Lin
Despite the importance of emphasizing the right psychotherapy treatment for an individual patient, assessing the outcome of the therapy session is equally crucial. Evidence showed…
MAP-Music2Vec: A Simple and Effective Baseline for Self-Supervised Music Audio Representation Learning
Yizhi Li, Ruibin Yuan, Ge Zhang +11
The deep learning community has witnessed an exponentially growing interest in self-supervised learning (SSL). However, it still remains unexplored how to build a framework for lea…
HERB: Measuring Hierarchical Regional Bias in Pre-trained Language Models
Yizhi Li, Ge Zhang, Bohao Yang +4
Fairness has become a trending topic in natural language processing (NLP), which addresses biases targeting certain social groups such as genders and religions. However, regional b…
Improving Variational Autoencoders with Density Gap-based Regularization
Jianfei Zhang, Jun Bai, Chenghua Lin +2
Variational autoencoders (VAEs) are one of the powerful unsupervised learning frameworks in NLP for latent representation learning and latent-directed generation. The classic optim…
EtriCA: Event-Triggered Context-Aware Story Generation Augmented by Cross Attention
Chen Tang, Chenghua Lin, Henglin Huang +2
One of the key challenges of automatic story generation is how to generate a long narrative that can maintain fluency, relevance, and coherence. Despite recent progress, current st…
Improving Chinese Story Generation via Awareness of Syntactic Dependencies and Semantics
Henglin Huang, Chen Tang, Tyler Loakman +2
Story generation aims to generate a long narrative conditioned on a given input. In spite of the success of prior works with the application of pre-trained models, current neural m…
NGEP: A Graph-based Event Planning Framework for Story Generation
Chen Tang, Zhihao Zhang, Tyler Loakman +2
To improve the performance of long text generation, recent studies have leveraged automatically planned event structures (i.e. storylines) to guide story generation. Such prior wor…
Terminology-aware Medical Dialogue Generation
Chen Tang, Hongbo Zhang, Tyler Loakman +2
Medical dialogue generation aims to generate responses according to a history of dialogue turns between doctors and patients. Unlike open-domain dialogue generation, this requires…