333 citations · 779 across the 60 of their papers we have counts for
67 papers
Few-Shot Data-to-Text Generation via Unified Representation and Multi-Source Learning
Alexander Hanbo Li, Mingyue Shang, Evangelia Spiliopoulou +9
We present a novel approach for structured data-to-text generation that addresses the limitations of existing methods that primarily focus on specific types of structured data. Our…
ParaGuide: Guided Diffusion Paraphrasers for Plug-and-Play Textual Style Transfer
Zachary Horvitz, Ajay Patel, Chris Callison-Burch +2
Textual style transfer is the task of transforming stylistic properties of text while preserving meaning. Target "styles" can be defined in numerous ways, ranging from single attri…
Do Models Explain Themselves? Counterfactual Simulatability of Natural Language Explanations
Yanda Chen, Ruiqi Zhong, Narutatsu Ri +5
Large language models (LLMs) are trained to imitate humans to explain human decisions. However, do LLMs explain themselves? Can they help humans build mental models of how LLMs pro…
Check-COVID: Fact-Checking COVID-19 News Claims with Scientific Evidence
Gengyu Wang, Kate Harwood, Lawrence Chillrud +3
We present a new fact-checking benchmark, Check-COVID, that requires systems to verify claims about COVID-19 from news using evidence from scientific articles. This approach to fac…
Generating EDU Extracts for Plan-Guided Summary Re-Ranking
Griffin Adams, Alexander R. Fabbri, Faisal Ladhak +2
Two-step approaches, in which summary candidates are generated-then-reranked to return a single summary, can improve ROUGE scores over the standard single-step approach. Yet, stand…
Unsupervised Selective Rationalization with Noise Injection
Adam Storek, Melanie Subbiah, Kathleen McKeown
A major issue with using deep learning models in sensitive applications is that they provide no explanation for their output. To address this problem, unsupervised selective ration…