Lingfei Wu

DynGraph2Seq: Dynamic-Graph-to-Sequence Interpretable Learning for Health Stage Prediction in Online Health Forums

2019 · arXiv (Cornell University)

Online health communities such as the online breast cancer forum enable patients (i.e., users) to interact and help each other within various subforums, which are subsections of the main forum devoted to specific health topics. …

Knowledge Graph-Augmented Abstractive Summarization with Semantic-Driven Cloze Reward

2020 · arXiv (Cornell University)

Sequence-to-sequence models for abstractive summarization have been studied extensively, yet the generated summaries commonly suffer from fabricated content, and are often found to be near-extractive. We argue that, to address these issues, the summarizer should …

Sequential Search with Off-Policy Reinforcement Learning

2021

Recent years have seen a significant amount of interests in Sequential Recommendation (SR), which aims to understand and model the sequential user behaviors and the interactions between users and items over time. Surprisingly, despite the …

Automatic Product Copywriting for E-commerce

2022 · Proceedings of the AAAI Conference on Artificial Intelligence

Product copywriting is a critical component of e-commerce recommendation platforms. It aims to attract users' interest and improve user experience by highlighting product characteristics with textual descriptions. In this paper, we report our experience deploying …

Graph Neural Networks: Foundation, Frontiers and Applications

2022 · Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the recent years. Graph neural networks, also known as deep learning on graphs, graph representation learning, or geometric deep learning, have …

Graph Neural Networks for Natural Language Processing: A Survey

2023 · Foundations and Trends® in Machine Learning

Deep learning has become the dominant approach in addressing various tasks in Natural Language Processing (NLP). Although text inputs are typically represented as a sequence of tokens, there is a rich variety of NLP problems …

Vague Preference Policy Learning for Conversational Recommendation

2023 · arXiv (Cornell University)

Conversational recommendation systems (CRS) commonly assume users have clear preferences, leading to potential over-filtering of relevant alternatives. However, users often exhibit vague, non-binary preferences. We introduce the Vague Preference Multi-round Conversational Recommendation (VPMCR) scenario, employing …

AdaCCD: Adaptive Semantic Contrasts Discovery Based Cross Lingual Adaptation for Code Clone Detection

2023 · arXiv (Cornell University)

Code Clone Detection, which aims to retrieve functionally similar programs from large code bases, has been attracting increasing attention. Modern software often involves a diverse range of programming languages. However, current code clone detection methods …

Graph2Seq: Graph to Sequence Learning with Attention-based Neural Networks

2018 · arXiv (Cornell University)

The celebrated Sequence to Sequence learning (Seq2Seq) technique and its numerous variants achieve excellent performance on many tasks. However, many machine learning tasks have inputs naturally represented as graphs; existing Seq2Seq models face a significant …

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