35 citations · 78 across the 12 of their papers we have counts for
19 papers · 1 filter
RAVEN: In-Context Learning with Retrieval-Augmented Encoder-Decoder Language Models
Jie Huang, Wei Ping, Peng Xu +3
In this paper, we investigate the in-context learning ability of retrieval-augmented encoder-decoder language models. We first conduct a comprehensive analysis of existing models a…
Evaluating Parameter Efficient Learning for Generation
Peng Xu, Mostofa Patwary, Shrimai Prabhumoye +6
Parameter efficient learning methods (PERMs) have recently gained significant attention as they provide an efficient way for pre-trained language models (PLMs) to adapt to a downst…
QA4QG: Using Question Answering to Constrain Multi-Hop Question Generation
Dan Su, Peng Xu, Pascale Fung
Multi-hop question generation (MQG) aims to generate complex questions which require reasoning over multiple pieces of information of the input passage. Most existing work on MQG h…
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…
BiToD: A Bilingual Multi-Domain Dataset For Task-Oriented Dialogue Modeling
Zhaojiang Lin, Andrea Madotto, Genta Indra Winata +5
Task-oriented dialogue (ToD) benchmarks provide an important avenue to measure progress and develop better conversational agents. However, existing datasets for end-to-end ToD mode…