10 citations · 23 across the 5 of their papers we have counts for
7 papers
Rethinking Clinical Relevance in Chest X-ray Machine Learning: How Evaluation References Define Performance
Panagiotis Fytas, Ian Selby, Clemens Karner +14
Chest X-ray (CXR) machine learning relies heavily on automated evaluation using reference standards that aim to approximate clinical judgment. However, commonly used report-derived…
Few-Shot Table-to-Text Generation with Prototype Memory
Yixuan Su, Zaiqiao Meng, Simon Baker +1
Neural table-to-text generation models have achieved remarkable progress on an array of tasks. However, due to the data-hungry nature of neural models, their performances strongly…
Non-Autoregressive Text Generation with Pre-trained Language Models
Yixuan Su, Deng Cai, Yan Wang +4
Non-autoregressive generation (NAG) has recently attracted great attention due to its fast inference speed. However, the generation quality of existing NAG models still lags behind…
Dialogue Response Selection with Hierarchical Curriculum Learning
Yixuan Su, Deng Cai, Qingyu Zhou +6
We study the learning of a matching model for dialogue response selection. Motivated by the recent finding that models trained with random negative samples are not ideal in real-wo…
Prototype-to-Style: Dialogue Generation with Style-Aware Editing on Retrieval Memory
Yixuan Su, Yan Wang, Simon Baker +4
The ability of a dialog system to express prespecified language style during conversations has a direct, positive impact on its usability and on user satisfaction. We introduce a n…
Stylistic Dialogue Generation via Information-Guided Reinforcement Learning Strategy
Yixuan Su, Deng Cai, Yan Wang +4
Stylistic response generation is crucial for building an engaging dialogue system for industrial use. While it has attracted much research interest, existing methods often generate…