activity
20162024
most citedAutomatic Labelling of Topics with Neural Embeddings

13 citations · 38 across the 11 of their papers we have counts for

collaborators

7 papers

cs.CL2023

Unsupervised Paraphrasing of Multiword Expressions

Takashi Wada, Yuji Matsumoto, Timothy Baldwin +1

We propose an unsupervised approach to paraphrasing multiword expressions (MWEs) in context. Our model employs only monolingual corpus data and pre-trained language models (without…

cs.CL2023

Annotating and Detecting Fine-grained Factual Errors for Dialogue Summarization

Rongxin Zhu, Jianzhong Qi, Jey Han Lau

A series of datasets and models have been proposed for summaries generated for well-formatted documents such as news articles. Dialogue summaries, however, have been under explored…

cs.CL202313 cited

MetaTroll: Few-shot Detection of State-Sponsored Trolls with Transformer Adapters

Lin Tian, Xiuzhen Zhang, Jey Han Lau

State-sponsored trolls are the main actors of influence campaigns on social media and automatic troll detection is important to combat misinformation at scale. Existing troll detec…

cs.CL20231 cited

Compressed Heterogeneous Graph for Abstractive Multi-Document Summarization

Miao Li, Jianzhong Qi, Jey Han Lau

Multi-document summarization (MDS) aims to generate a summary for a number of related documents. We propose HGSUM, an MDS model that extends an encoder-decoder architecture, to inc…

cs.CL2021

Findings on Conversation Disentanglement

Rongxin Zhu, Jey Han Lau, Jianzhong Qi

Conversation disentanglement, the task to identify separate threads in conversations, is an important pre-processing step in multi-party conversational NLP applications such as con…

cs.CL201613 cited

Automatic Labelling of Topics with Neural Embeddings

Shraey Bhatia, Jey Han Lau, Timothy Baldwin

Topics generated by topic models are typically represented as list of terms. To reduce the cognitive overhead of interpreting these topics for end-users, we propose labelling a top…