52 citations · 115 across the 11 of their papers we have counts for
12 papers
Blending Reward Functions via Few Expert Demonstrations for Faithful and Accurate Knowledge-Grounded Dialogue Generation
Wanyu Du, Yangfeng Ji
The development of trustworthy conversational information-seeking systems relies on dialogue models that can generate faithful and accurate responses based on relevant knowledge te…
Improving Iterative Text Revision by Learning Where to Edit from Other Revision Tasks
Zae Myung Kim, Wanyu Du, Vipul Raheja +2
Iterative text revision improves text quality by fixing grammatical errors, rephrasing for better readability or contextual appropriateness, or reorganizing sentence structures thr…
Self-training with Two-phase Self-augmentation for Few-shot Dialogue Generation
Wanyu Du, Hanjie Chen, Yangfeng Ji
In task-oriented dialogue systems, response generation from meaning representations (MRs) often suffers from limited training examples, due to the high cost of annotating MR-to-Tex…
Diverse Text Generation via Variational Encoder-Decoder Models with Gaussian Process Priors
Wanyu Du, Jianqiao Zhao, Liwei Wang +1
Generating high quality texts with high diversity is important for many NLG applications, but current methods mostly focus on building deterministic models to generate higher quali…
Read, Revise, Repeat: A System Demonstration for Human-in-the-loop Iterative Text Revision
Wanyu Du, Zae Myung Kim, Vipul Raheja +2
Revision is an essential part of the human writing process. It tends to be strategic, adaptive, and, more importantly, iterative in nature. Despite the success of large language mo…
Understanding Iterative Revision from Human-Written Text
Wanyu Du, Vipul Raheja, Dhruv Kumar +3
Writing is, by nature, a strategic, adaptive, and more importantly, an iterative process. A crucial part of writing is editing and revising the text. Previous works on text revisio…
FlowEval: A Consensus-Based Dialogue Evaluation Framework Using Segment Act Flows
Jianqiao Zhao, Yanyang Li, Wanyu Du +4
Despite recent progress in open-domain dialogue evaluation, how to develop automatic metrics remains an open problem. We explore the potential of dialogue evaluation featuring dial…
Explaining Predictive Uncertainty by Looking Back at Model Explanations
Hanjie Chen, Wanyu Du, Yangfeng Ji
Predictive uncertainty estimation of pre-trained language models is an important measure of how likely people can trust their predictions. However, little is known about what makes…
SideControl: Controlled Open-domain Dialogue Generation via Additive Side Networks
Wanyu Du, Yangfeng Ji
Transformer-based pre-trained language models boost the performance of open-domain dialogue systems. Prior works leverage Transformer-based pre-trained language models to generate…
Contextualizing Variation in Text Style Transfer Datasets
Stephanie Schoch, Wanyu Du, Yangfeng Ji
Text style transfer involves rewriting the content of a source sentence in a target style. Despite there being a number of style tasks with available data, there has been limited s…
The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics
Sebastian Gehrmann, Tosin Adewumi, Karmanya Aggarwal +53
We introduce GEM, a living benchmark for natural language Generation (NLG), its Evaluation, and Metrics. Measuring progress in NLG relies on a constantly evolving ecosystem of auto…
An Empirical Comparison on Imitation Learning and Reinforcement Learning for Paraphrase Generation
Wanyu Du, Yangfeng Ji
Generating paraphrases from given sentences involves decoding words step by step from a large vocabulary. To learn a decoder, supervised learning which maximizes the likelihood of…