895 citations · 1.4k across the 40 of their papers we have counts for
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Mixup-Transformer: Dynamic Data Augmentation for NLP Tasks
Lichao Sun, Congying Xia, Wenpeng Yin +3
Mixup is the latest data augmentation technique that linearly interpolates input examples and the corresponding labels. It has shown strong effectiveness in image classification by…
Universal Natural Language Processing with Limited Annotations: Try Few-shot Textual Entailment as a Start
Wenpeng Yin, Nazneen Fatema Rajani, Dragomir Radev +2
A standard way to address different NLP problems is by first constructing a problem-specific dataset, then building a model to fit this dataset. To build the ultimate artificial in…
Pairwise Representation Learning for Event Coreference
Xiaodong Yu, Wenpeng Yin, Dan Roth
Natural Language Processing tasks such as resolving the coreference of events require understanding the relations between two text snippets. These tasks are typically formulated as…
Meta-learning for Few-shot Natural Language Processing: A Survey
Wenpeng Yin
Few-shot natural language processing (NLP) refers to NLP tasks that are accompanied with merely a handful of labeled examples. This is a real-world challenge that an AI system must…
CO-Search: COVID-19 Information Retrieval with Semantic Search, Question Answering, and Abstractive Summarization
Andre Esteva, Anuprit Kale, Romain Paulus +4
The COVID-19 global pandemic has resulted in international efforts to understand, track, and mitigate the disease, yielding a significant corpus of COVID-19 and SARS-CoV-2-related…
Adv-BERT: BERT is not robust on misspellings! Generating nature adversarial samples on BERT
Lichao Sun, Kazuma Hashimoto, Wenpeng Yin +4
There is an increasing amount of literature that claims the brittleness of deep neural networks in dealing with adversarial examples that are created maliciously. It is unclear, ho…