Hui Wan

Rewarding Smatch: Transition-Based AMR Parsing with Reinforcement Learning

2019 · arXiv (Cornell University)

Our work involves enriching the Stack-LSTM transition-based AMR parser (Ballesteros and Al-Onaizan, 2017) by augmenting training with Policy Learning and rewarding the Smatch score of sampled graphs. In addition, we also combined several AMR-to-text alignments …

Explaining Neural Network Predictions on Sentence Pairs via Learning Word-Group Masks

2021 · arXiv (Cornell University)

Explaining neural network models is important for increasing their trustworthiness in real-world applications. Most existing methods generate post-hoc explanations for neural network models by identifying individual feature attributions or detecting interactions between adjacent features. However, …

Fast and Light-Weight Answer Text Retrieval in Dialogue Systems

2022 · arXiv (Cornell University)

Dialogue systems can benefit from being able to search through a corpus of text to find information relevant to user requests, especially when encountering a request for which no manually curated response is available. The …