31 citations · 129 across the 17 of their papers we have counts for
14 papers · 1 filter
Improving (Dis)agreement Detection with Inductive Social Relation Information From Comment-Reply Interactions
Yun Luo, Zihan Liu, Stan Z. Li +1
(Dis)agreement detection aims to identify the authors' attitudes or positions (\textit{agree, disagree, neutral}) towards a specific text. It is limited for existing methods merely…
Exploiting Sentiment and Common Sense for Zero-shot Stance Detection
Yun Luo, Zihan Liu, Yuefeng Shi +2
The stance detection task aims to classify the stance toward given documents and topics. Since the topics can be implicit in documents and unseen in training data for zero-shot set…
Multi-Stage Prompting for Knowledgeable Dialogue Generation
Zihan Liu, Mostofa Patwary, Ryan Prenger +4
Existing knowledge-grounded dialogue systems typically use finetuned versions of a pretrained language model (LM) and large-scale knowledge bases. These models typically fail to ge…
ASCEND: A Spontaneous Chinese-English Dataset for Code-switching in Multi-turn Conversation
Holy Lovenia, Samuel Cahyawijaya, Genta Indra Winata +11
Code-switching is a speech phenomenon occurring when a speaker switches language during a conversation. Despite the spontaneous nature of code-switching in conversational spoken la…
NER-BERT: A Pre-trained Model for Low-Resource Entity Tagging
Zihan Liu, Feijun Jiang, Yuxiang Hu +2
Named entity recognition (NER) models generally perform poorly when large training datasets are unavailable for low-resource domains. Recently, pre-training a large-scale language…
Vision Guided Generative Pre-trained Language Models for Multimodal Abstractive Summarization
Tiezheng Yu, Wenliang Dai, Zihan Liu +1
Multimodal abstractive summarization (MAS) models that summarize videos (vision modality) and their corresponding transcripts (text modality) are able to extract the essential info…
CAiRE in DialDoc21: Data Augmentation for Information-Seeking Dialogue System
Etsuko Ishii, Yan Xu, Genta Indra Winata +5
Information-seeking dialogue systems, including knowledge identification and response generation, aim to respond to users with fluent, coherent, and informative responses based on…
X2Parser: Cross-Lingual and Cross-Domain Framework for Task-Oriented Compositional Semantic Parsing
Zihan Liu, Genta Indra Winata, Peng Xu +1
Task-oriented compositional semantic parsing (TCSP) handles complex nested user queries and serves as an essential component of virtual assistants. Current TCSP models rely on nume…
Continual Mixed-Language Pre-Training for Extremely Low-Resource Neural Machine Translation
Zihan Liu, Genta Indra Winata, Pascale Fung
The data scarcity in low-resource languages has become a bottleneck to building robust neural machine translation systems. Fine-tuning a multilingual pre-trained model (e.g., mBART…
Retrieval-Free Knowledge-Grounded Dialogue Response Generation with Adapters
Yan Xu, Etsuko Ishii, Samuel Cahyawijaya +5
To diversify and enrich generated dialogue responses, knowledge-grounded dialogue has been investigated in recent years. The existing methods tackle the knowledge grounding challen…
AdaptSum: Towards Low-Resource Domain Adaptation for Abstractive Summarization
Tiezheng Yu, Zihan Liu, Pascale Fung
State-of-the-art abstractive summarization models generally rely on extensive labeled data, which lowers their generalization ability on domains where such data are not available.…
Are Multilingual Models Effective in Code-Switching?
Genta Indra Winata, Samuel Cahyawijaya, Zihan Liu +3
Multilingual language models have shown decent performance in multilingual and cross-lingual natural language understanding tasks. However, the power of these multilingual models i…
Multimodal End-to-End Sparse Model for Emotion Recognition
Wenliang Dai, Samuel Cahyawijaya, Zihan Liu +1
Existing works on multimodal affective computing tasks, such as emotion recognition, generally adopt a two-phase pipeline, first extracting feature representations for each single…
Exploring Fine-tuning Techniques for Pre-trained Cross-lingual Models via Continual Learning
Zihan Liu, Genta Indra Winata, Andrea Madotto +1
Recently, fine-tuning pre-trained language models (e.g., multilingual BERT) to downstream cross-lingual tasks has shown promising results. However, the fine-tuning process inevitab…