19 citations · 31 across the 9 of their papers we have counts for
6 papers · 1 filter
Hardness-guided domain adaptation to recognise biomedical named entities under low-resource scenarios
Ngoc Dang Nguyen, Lan Du, Wray Buntine +2
Domain adaptation is an effective solution to data scarcity in low-resource scenarios. However, when applied to token-level tasks such as bioNER, domain adaptation methods often su…
Learning Semantic Textual Similarity via Topic-informed Discrete Latent Variables
Erxin Yu, Lan Du, Yuan Jin +2
Recently, discrete latent variable models have received a surge of interest in both Natural Language Processing (NLP) and Computer Vision (CV), attributed to their comparable perfo…
Multilingual Neural Machine Translation:Can Linguistic Hierarchies Help?
Fahimeh Saleh, Wray Buntine, Gholamreza Haffari +1
Multilingual Neural Machine Translation (MNMT) trains a single NMT model that supports translation between multiple languages, rather than training separate models for different la…
Neural Attention-Aware Hierarchical Topic Model
Yuan Jin, He Zhao, Ming Liu +2
Neural topic models (NTMs) apply deep neural networks to topic modelling. Despite their success, NTMs generally ignore two important aspects: (1) only document-level word count inf…
Transformer over Pre-trained Transformer for Neural Text Segmentation with Enhanced Topic Coherence
Kelvin Lo, Yuan Jin, Weicong Tan +3
This paper proposes a transformer over transformer framework, called Transformer, to perform neural text segmentation. It consists of two components: bottom-level sentence enco…
MetaLDA: a Topic Model that Efficiently Incorporates Meta information
He Zhao, Lan Du, Wray Buntine +1
Besides the text content, documents and their associated words usually come with rich sets of meta informa- tion, such as categories of documents and semantic/syntactic features of…