activity
20172022
most citedLearning to Rank Question Answer Pairs with Holographic Dual LSTM Architecture

64 citations · 228 across the 10 of their papers we have counts for

collaborators

20 papers

cs.CL20221 cited

Textual Manifold-based Defense Against Natural Language Adversarial Examples

Dang Minh Nguyen, Luu Anh Tuan

Recent studies on adversarial images have shown that they tend to leave the underlying low-dimensional data manifold, making them significantly more challenging for current models…

cs.CL2019

Capturing Greater Context for Question Generation

Luu Anh Tuan, Darsh J Shah, Regina Barzilay

Automatic question generation can benefit many applications ranging from dialogue systems to reading comprehension. While questions are often asked with respect to long documents,…

cs.CL201917 cited

Lightweight and Efficient Neural Natural Language Processing with Quaternion Networks

Yi Tay, Aston Zhang, Luu Anh Tuan +5

Many state-of-the-art neural models for NLP are heavily parameterized and thus memory inefficient. This paper proposes a series of lightweight and memory efficient neural architect…

cs.CL201916 cited

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

Yi Tay, Shuohang Wang, Luu Anh Tuan +6

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 Point…

cs.CL2018

Recurrently Controlled Recurrent Networks

Yi Tay, Luu Anh Tuan, Siu Cheung Hui

Recurrent neural networks (RNNs) such as long short-term memory and gated recurrent units are pivotal building blocks across a broad spectrum of sequence modeling problems. This pa…

cs.CV2018

Holistic Multi-modal Memory Network for Movie Question Answering

Anran Wang, Anh Tuan Luu, Chuan-Sheng Foo +3

Answering questions according to multi-modal context is a challenging problem as it requires a deep integration of different data sources. Existing approaches only employ partial i…