Siu Cheung Hui

Simple and Effective Curriculum Pointer-Generator Networks for Reading Comprehension over Long Narratives

2019 · arXiv (Cornell University)

This paper tackles the problem of reading comprehension over long narratives where documents easily span over thousands of tokens. We propose a curriculum learning (CL) based Pointer-Generator framework for reading/sampling over large documents, enabling diverse …

Multi-Task Neural Network for Non-discrete Attribute Prediction in Knowledge Graphs

2017

Many popular knowledge graphs such as Freebase, YAGO or DBPedia maintain a list of non-discrete attributes for each entity. Intuitively, these attributes such as height, price or population count are able to richly characterize entities …

GranCATs: Cross-Lingual Enhancement through Granularity-Specific Contrastive Adapters

2023

Multilingual language models (MLLMs) have demonstrated remarkable success in various cross-lingual downstream tasks, facilitating the transfer of knowledge across numerous languages, whereas this transfer is not universally effective. Our study reveals that while existing MLLMs …

Final: Combining First-Order Logic With Natural Logic for Question Answering

2025 · IEEE Transactions on Knowledge and Data Engineering

Many question-answering problems can be approached as textual entailment tasks, where the hypotheses are formed by the question and candidate answers, and the premises are derived from an external knowledge base. However, current neural methods …

Learning to Rank Question Answer Pairs with Holographic Dual LSTM Architecture

2017

We describe a new deep learning architecture for learning to rank question answer pairs. Our approach extends the long short-term memory (LSTM) network with holographic composition to model the relationship between question and answer representations. …

Latent Relational Metric Learning via Memory-based Attention for Collaborative Ranking

2018

This paper proposes a new neural architecture for collaborative ranking with implicit feedback. Our model, LRML (Latent Relational Metric Learning) is a novel metric learning approach for recommendation. More specifically, instead of simple push-pull mechanisms …

Multi-Pointer Co-Attention Networks for Recommendation

2018

Many recent state-of-the-art recommender systems such as D-ATT, TransNet and DeepCoNN exploit reviews for representation learning. This paper proposes a new neural architecture for recommendation with reviews. Our model operates on a multi-hierarchical paradigm and …