11 citations · 18 across the 5 of their papers we have counts for
8 papers
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…
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…
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.…