2.5k citations · 4.3k across the 50 of their papers we have counts for
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A Simple but Tough-to-Beat Data Augmentation Approach for Natural Language Understanding and Generation
Dinghan Shen, Mingzhi Zheng, Yelong Shen +2
Adversarial training has been shown effective at endowing the learned representations with stronger generalization ability. However, it typically requires expensive computation to…
CoDA: Contrast-enhanced and Diversity-promoting Data Augmentation for Natural Language Understanding
Yanru Qu, Dinghan Shen, Yelong Shen +3
Data augmentation has been demonstrated as an effective strategy for improving model generalization and data efficiency. However, due to the discrete nature of natural language, de…
Improving Self-supervised Pre-training via a Fully-Explored Masked Language Model
Mingzhi Zheng, Dinghan Shen, Yelong Shen +2
Masked Language Model (MLM) framework has been widely adopted for self-supervised language pre-training. In this paper, we argue that randomly sampled masks in MLM would lead to un…
Generation-Augmented Retrieval for Open-domain Question Answering
Yuning Mao, Pengcheng He, Xiaodong Liu +4
We propose Generation-Augmented Retrieval (GAR) for answering open-domain questions, which augments a query through text generation of heuristically discovered relevant contexts wi…
Recurrent Chunking Mechanisms for Long-Text Machine Reading Comprehension
Hongyu Gong, Yelong Shen, Dian Yu +2
In this paper, we study machine reading comprehension (MRC) on long texts, where a model takes as inputs a lengthy document and a question and then extracts a text span from the do…
MART: Memory-Augmented Recurrent Transformer for Coherent Video Paragraph Captioning
Jie Lei, Liwei Wang, Yelong Shen +3
Generating multi-sentence descriptions for videos is one of the most challenging captioning tasks due to its high requirements for not only visual relevance but also discourse-base…