most citedOn the Generation of Medical Dialogues for COVID-19

10 citations · 27 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CL20202 cited

Perception Score, A Learned Metric for Open-ended Text Generation Evaluation

Jing Gu, Qingyang Wu, Zhou Yu

Automatic evaluation for open-ended natural language generation tasks remains a challenge. Existing metrics such as BLEU show a low correlation with human judgment. We propose a no…

cs.CL202010 cited

On the Generation of Medical Dialogues for COVID-19

Wenmian Yang, Guangtao Zeng, Bowen Tan +9

Under the pandemic of COVID-19, people experiencing COVID19-related symptoms or exposed to risk factors have a pressing need to consult doctors. Due to hospital closure, a lot of c…

cs.CL20208 cited

A Tailored Pre-Training Model for Task-Oriented Dialog Generation

Jing Gu, Qingyang Wu, Chongruo Wu +2

The recent success of large pre-trained language models such as BERT and GPT-2 has suggested the effectiveness of incorporating language priors in downstream dialog generation task…

cs.CL20197 cited

Importance-Aware Learning for Neural Headline Editing

Qingyang Wu, Lei Li, Hao Zhou +2

Many social media news writers are not professionally trained. Therefore, social media platforms have to hire professional editors to adjust amateur headlines to attract more reade…

cs.LG2019

Quantifying Intrinsic Uncertainty in Classification via Deep Dirichlet Mixture Networks

Qingyang Wu, He Li, Lexin Li +1

With the widespread success of deep neural networks in science and technology, it is becoming increasingly important to quantify the uncertainty of the predictions produced by deep…