activity
20192022
most citedLanguage Models as Few-Shot Learner for Task-Oriented Dialogue Systems

36 citations · 117 across the 11 of their papers we have counts for

collaborators

21 papers

cs.LG2022

SNP2Vec: Scalable Self-Supervised Pre-Training for Genome-Wide Association Study

Samuel Cahyawijaya, Tiezheng Yu, Zihan Liu +4

Self-supervised pre-training methods have brought remarkable breakthroughs in the understanding of text, image, and speech. Recent developments in genomics has also adopted these p…

cs.CL202022 cited

CrossNER: Evaluating Cross-Domain Named Entity Recognition

Zihan Liu, Yan Xu, Tiezheng Yu +5

Cross-domain named entity recognition (NER) models are able to cope with the scarcity issue of NER samples in target domains. However, most of the existing NER benchmarks lack doma…

cs.CL2020

Modality-Transferable Emotion Embeddings for Low-Resource Multimodal Emotion Recognition

Wenliang Dai, Zihan Liu, Tiezheng Yu +1

Despite the recent achievements made in the multi-modal emotion recognition task, two problems still exist and have not been well investigated: 1) the relationship between differen…

cs.CL20202 cited

Cross-lingual Spoken Language Understanding with Regularized Representation Alignment

Zihan Liu, Genta Indra Winata, Peng Xu +2

Despite the promising results of current cross-lingual models for spoken language understanding systems, they still suffer from imperfect cross-lingual representation alignments be…

cs.CL2020

Learning Knowledge Bases with Parameters for Task-Oriented Dialogue Systems

Andrea Madotto, Samuel Cahyawijaya, Genta Indra Winata +4

Task-oriented dialogue systems are either modularized with separate dialogue state tracking (DST) and management steps or end-to-end trainable. In either case, the knowledge base (…

cs.CL202027 cited

EmoGraph: Capturing Emotion Correlations using Graph Networks

Peng Xu, Zihan Liu, Genta Indra Winata +2

Most emotion recognition methods tackle the emotion understanding task by considering individual emotion independently while ignoring their fuzziness nature and the interconnection…