21 citations · 112 across the 30 of their papers we have counts for
16 papers · 1 filter
Eliciting Knowledge from Large Pre-Trained Models for Unsupervised Knowledge-Grounded Conversation
Yanyang Li, Jianqiao Zhao, Michael R. Lyu +1
Recent advances in large-scale pre-training provide large models with the potential to learn knowledge from the raw text. It is thus natural to ask whether it is possible to levera…
Text Revision by On-the-Fly Representation Optimization
Jingjing Li, Zichao Li, Tao Ge +2
Text revision refers to a family of natural language generation tasks, where the source and target sequences share moderate resemblance in surface form but differentiate in attribu…
Understanding and Improving Sequence-to-Sequence Pretraining for Neural Machine Translation
Wenxuan Wang, Wenxiang Jiao, Yongchang Hao +4
In this paper, we present a substantial step in better understanding the SOTA sequence-to-sequence (Seq2Seq) pretraining for neural machine translation~(NMT). We focus on studying…
Towards Efficient Post-training Quantization of Pre-trained Language Models
Haoli Bai, Lu Hou, Lifeng Shang +3
Network quantization has gained increasing attention with the rapid growth of large pre-trained language models~(PLMs). However, most existing quantization methods for PLMs follow…
Self-Training Sampling with Monolingual Data Uncertainty for Neural Machine Translation
Wenxiang Jiao, Xing Wang, Zhaopeng Tu +3
Self-training has proven effective for improving NMT performance by augmenting model training with synthetic parallel data. The common practice is to construct synthetic data based…
Discern: Discourse-Aware Entailment Reasoning Network for Conversational Machine Reading
Yifan Gao, Chien-Sheng Wu, Jingjing Li +5
Document interpretation and dialog understanding are the two major challenges for conversational machine reading. In this work, we propose Discern, a discourse-aware entailment rea…