88 citations · 131 across the 21 of their papers we have counts for
24 papers
On the Robustness of Knowledge Editing for Detoxification
Ming Dong, Shiyi Tang, Ziyan Peng +2
Knowledge-Editing-based (KE-based) detoxification has emerged as a promising approach for mitigating harmful behaviours in Large Language Models. Existing evaluations, however, lar…
How Do People Quantify Naturally: Evidence from Mandarin Picture Description
Yayun Zhang, Guanyi Chen, Fahime Same +2
Quantification is a fundamental component of everyday language use, yet little is known about how speakers decide whether and how to quantify in naturalistic production. We investi…
Do Large Language Models Judge Error Severity Like Humans?
Diege Sun, Guanyi Chen, Zhao Fan +2
Large Language Models (LLMs) are increasingly used as automated evaluators in natural language generation, yet it remains unclear whether they can accurately replicate human judgme…
Emotional Supporters often Use Multiple Strategies in a Single Turn
Xin Bai, Guanyi Chen, Tingting He +2
Emotional Support Conversations (ESC) are crucial for providing empathy, validation, and actionable guidance to individuals in distress. However, existing definitions of the ESC ta…
CCNU at SemEval-2025 Task 3: Leveraging Internal and External Knowledge of Large Language Models for Multilingual Hallucination Annotation
Xu Liu, Guanyi Chen
We present the system developed by the Central China Normal University (CCNU) team for the Mu-SHROOM shared task, which focuses on identifying hallucinations in question-answering…
Computational Modelling of Plurality and Definiteness in Chinese Noun Phrases
Yuqi Liu, Guanyi Chen, Kees van Deemter
Theoretical linguists have suggested that some languages (e.g., Chinese and Japanese) are "cooler" than other languages based on the observation that the intended meaning of phrase…
Intrinsic Task-based Evaluation for Referring Expression Generation
Guanyi Chen, Fahime Same, Kees van Deemter
Recently, a human evaluation study of Referring Expression Generation (REG) models had an unexpected conclusion: on \textsc{webnlg}, Referring Expressions (REs) generated by the st…
A Survey on Semantic Processing Techniques
Rui Mao, Kai He, Xulang Zhang +4
Semantic processing is a fundamental research domain in computational linguistics. In the era of powerful pre-trained language models and large language models, the advancement of…
GPTEval: A Survey on Assessments of ChatGPT and GPT-4
Rui Mao, Guanyi Chen, Xulang Zhang +2
The emergence of ChatGPT has generated much speculation in the press about its potential to disrupt social and economic systems. Its astonishing language ability has aroused strong…
Models of reference production: How do they withstand the test of time?
Fahime Same, Guanyi Chen, Kees van Deemter
In recent years, many NLP studies have focused solely on performance improvement. In this work, we focus on the linguistic and scientific aspects of NLP. We use the task of generat…
Assessing Neural Referential Form Selectors on a Realistic Multilingual Dataset
Guanyi Chen, Fahime Same, Kees van Deemter
Previous work on Neural Referring Expression Generation (REG) all uses WebNLG, an English dataset that has been shown to reflect a very limited range of referring expression (RE) u…
Understanding the Use of Quantifiers in Mandarin
Guanyi Chen, Kees van Deemter
We introduce a corpus of short texts in Mandarin, in which quantified expressions figure prominently. We illustrate the significance of the corpus by examining the hypothesis (know…
Accuracy on In-Domain Samples Matters When Building Out-of-Domain detectors: A Reply to Marek et al. (2021)
Yinhe Zheng, Guanyi Chen
We have noticed that Marek et al. (2021) try to re-implement our paper Zheng et al. (2020a) in their work "OodGAN: Generative Adversarial Network for Out-of-Domain Data Generation"…
Non-neural Models Matter: A Re-evaluation of Neural Referring Expression Generation Systems
Fahime Same, Guanyi Chen, Kees van Deemter
In recent years, neural models have often outperformed rule-based and classic Machine Learning approaches in NLG. These classic approaches are now often disregarded, for example wh…
What can Neural Referential Form Selectors Learn?
Guanyi Chen, Fahime Same, Kees van Deemter
Despite achieving encouraging results, neural Referring Expression Generation models are often thought to lack transparency. We probed neural Referential Form Selection (RFS) model…
Affective Decoding for Empathetic Response Generation
Chengkun Zeng, Guanyi Chen, Chenghua Lin +2
Understanding speaker's feelings and producing appropriate responses with emotion connection is a key communicative skill for empathetic dialogue systems. In this paper, we propose…
MMChat: Multi-Modal Chat Dataset on Social Media
Yinhe Zheng, Guanyi Chen, Xin Liu +1
Incorporating multi-modal contexts in conversation is important for developing more engaging dialogue systems. In this work, we explore this direction by introducing MMChat: a larg…
Highly Efficient Knowledge Graph Embedding Learning with Orthogonal Procrustes Analysis
Xutan Peng, Guanyi Chen, Chenghua Lin +1
Knowledge Graph Embeddings (KGEs) have been intensively explored in recent years due to their promise for a wide range of applications. However, existing studies focus on improving…
Improving Variational Autoencoder for Text Modelling with Timestep-Wise Regularisation
Ruizhe Li, Xiao Li, Guanyi Chen +1
The Variational Autoencoder (VAE) is a popular and powerful model applied to text modelling to generate diverse sentences. However, an issue known as posterior collapse (or KL loss…
Lessons from Computational Modelling of Reference Production in Mandarin and English
Guanyi Chen, Kees van Deemter
Referring expression generation (REG) algorithms offer computational models of the production of referring expressions. In earlier work, a corpus of referring expressions (REs) in…
Listener's Social Identity Matters in Personalised Response Generation
Guanyi Chen, Yinhe Zheng, Yupei Du
Personalised response generation enables generating human-like responses by means of assigning the generator a social identity. However, pragmatics theory suggests that human being…
Out-of-domain Detection for Natural Language Understanding in Dialog Systems
Yinhe Zheng, Guanyi Chen, Minlie Huang
Natural Language Understanding (NLU) is a vital component of dialogue systems, and its ability to detect Out-of-Domain (OOD) inputs is critical in practical applications, since the…
Personalized Dialogue Generation with Diversified Traits
Yinhe Zheng, Guanyi Chen, Minlie Huang +2
Endowing a dialogue system with particular personality traits is essential to deliver more human-like conversations. However, due to the challenge of embodying personality via lang…
A Dual-Attention Hierarchical Recurrent Neural Network for Dialogue Act Classification
Ruizhe Li, Chenghua Lin, Matthew Collinson +2
Recognising dialogue acts (DA) is important for many natural language processing tasks such as dialogue generation and intention recognition. In this paper, we propose a dual-atten…