941 citations · 2.8k across the 32 of their papers we have counts for
5 papers · 1 filter
MiniLMv2: Multi-Head Self-Attention Relation Distillation for Compressing Pretrained Transformers
Wenhui Wang, Hangbo Bao, Shaohan Huang +2
We generalize deep self-attention distillation in MiniLM (Wang et al., 2020) by only using self-attention relation distillation for task-agnostic compression of pretrained Transfor…
InfoXLM: An Information-Theoretic Framework for Cross-Lingual Language Model Pre-Training
Zewen Chi, Li Dong, Furu Wei +7
In this work, we present an information-theoretic framework that formulates cross-lingual language model pre-training as maximizing mutual information between multilingual-multi-gr…
Harvesting and Refining Question-Answer Pairs for Unsupervised QA
Zhongli Li, Wenhui Wang, Li Dong +2
Question Answering (QA) has shown great success thanks to the availability of large-scale datasets and the effectiveness of neural models. Recent research works have attempted to e…
UniLMv2: Pseudo-Masked Language Models for Unified Language Model Pre-Training
Hangbo Bao, Li Dong, Furu Wei +8
We propose to pre-train a unified language model for both autoencoding and partially autoregressive language modeling tasks using a novel training procedure, referred to as a pseud…
MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers
Wenhui Wang, Furu Wei, Li Dong +3
Pre-trained language models (e.g., BERT (Devlin et al., 2018) and its variants) have achieved remarkable success in varieties of NLP tasks. However, these models usually consist of…