1.5k citations · 1.8k across the 22 of their papers we have counts for
26 papers · 1 filter
Contrastive Document Representation Learning with Graph Attention Networks
Peng Xu, Xinchi Chen, Xiaofei Ma +2
Recent progress in pretrained Transformer-based language models has shown great success in learning contextual representation of text. However, due to the quadratic self-attention…
Attention-guided Generative Models for Extractive Question Answering
Peng Xu, Davis Liang, Zhiheng Huang +1
We propose a novel method for applying Transformer models to extractive question answering (QA) tasks. Recently, pretrained generative sequence-to-sequence (seq2seq) models have ac…
Multiplicative Position-aware Transformer Models for Language Understanding
Zhiheng Huang, Davis Liang, Peng Xu +1
Transformer models, which leverage architectural improvements like self-attention, perform remarkably well on Natural Language Processing (NLP) tasks. The self-attention mechanism…
Joint Text and Label Generation for Spoken Language Understanding
Yang Li, Ben Athiwaratkun, Cicero Nogueira dos Santos +1
Generalization is a central problem in machine learning, especially when data is limited. Using prior information to enforce constraints is the principled way of encouraging genera…
Improving Factual Consistency of Abstractive Summarization via Question Answering
Feng Nan, Cicero Nogueira dos Santos, Henghui Zhu +7
A commonly observed problem with the state-of-the art abstractive summarization models is that the generated summaries can be factually inconsistent with the input documents. The f…
Generative Context Pair Selection for Multi-hop Question Answering
Dheeru Dua, Cicero Nogueira dos Santos, Patrick Ng +4
Compositional reasoning tasks like multi-hop question answering, require making latent decisions to get the final answer, given a question. However, crowdsourced datasets often cap…