276 citations · 2k across the 138 of their papers we have counts for
32 papers · 1 filter
Improving Cross-task Generalization of Unified Table-to-text Models with Compositional Task Configurations
Jifan Chen, Yuhao Zhang, Lan Liu +5
There has been great progress in unifying various table-to-text tasks using a single encoder-decoder model trained via multi-task learning (Xie et al., 2022). However, existing met…
CausalDialogue: Modeling Utterance-level Causality in Conversations
Yi-Lin Tuan, Alon Albalak, Wenda Xu +4
Despite their widespread adoption, neural conversation models have yet to exhibit natural chat capabilities with humans. In this research, we examine user utterances as causes and…
Tokenization Consistency Matters for Generative Models on Extractive NLP Tasks
Kaiser Sun, Peng Qi, Yuhao Zhang +3
Generative models have been widely applied to solve extractive tasks, where parts of the input is extracted to form the desired output, and achieved significant success. For exampl…
Foveate, Attribute, and Rationalize: Towards Physically Safe and Trustworthy AI
Alex Mei, Sharon Levy, William Yang Wang
Users' physical safety is an increasing concern as the market for intelligent systems continues to grow, where unconstrained systems may recommend users dangerous actions that can…
SESCORE2: Learning Text Generation Evaluation via Synthesizing Realistic Mistakes
Wenda Xu, Xian Qian, Mingxuan Wang +2
Is it possible to train a general metric for evaluating text generation quality without human annotated ratings? Existing learned metrics either perform unsatisfactorily across tex…
Training-Free Structured Diffusion Guidance for Compositional Text-to-Image Synthesis
Weixi Feng, Xuehai He, Tsu-Jui Fu +6
Large-scale diffusion models have achieved state-of-the-art results on text-to-image synthesis (T2I) tasks. Despite their ability to generate high-quality yet creative images, we o…