24 citations · 105 across the 16 of their papers we have counts for
4 papers · 1 filter
Inverse Reinforcement Learning for Text Summarization
Yu Fu, Deyi Xiong, Yue Dong
We introduce inverse reinforcement learning (IRL) as an effective paradigm for training abstractive summarization models, imitating human summarization behaviors. Our IRL model est…
Source-Free Domain Adaptation for Question Answering with Masked Self-training
M. Yin, B. Wang, Y. Dong +1
Most previous unsupervised domain adaptation (UDA) methods for question answering(QA) require access to source domain data while fine-tuning the model for the target domain. Source…
Text Generation with Text-Editing Models
Eric Malmi, Yue Dong, Jonathan Mallinson +7
Text-editing models have recently become a prominent alternative to seq2seq models for monolingual text-generation tasks such as grammatical error correction, simplification, and s…
Faithful to the Document or to the World? Mitigating Hallucinations via Entity-linked Knowledge in Abstractive Summarization
Yue Dong, John Wieting, Pat Verga
Despite recent advances in abstractive summarization, current summarization systems still suffer from content hallucinations where models generate text that is either irrelevant or…