Kevin Duh

How Do Source-side Monolingual Word Embeddings Impact Neural Machine Translation?

2018 · arXiv (Cornell University)

Using pre-trained word embeddings as input layer is a common practice in many natural language processing (NLP) tasks, but it is largely neglected for neural machine translation (NMT). In this paper, we conducted a systematic …

Query Expansion for Cross-Language Question Re-Ranking

2019 · arXiv (Cornell University)

Community question-answering (CQA) platforms have become very popular forums for asking and answering questions daily. While these forums are rich repositories of community knowledge, they present challenges for finding relevant answers and similar questions, due …

ESPnet-ST: All-in-One Speech Translation Toolkit

2020 · arXiv (Cornell University)

We present ESPnet-ST, which is designed for the quick development of speech-to-speech translation systems in a single framework. ESPnet-ST is a new project inside end-to-end speech processing toolkit, ESPnet, which integrates or newly implements automatic …

Representation Learning Using Multi-Task Deep Neural Networks for Semantic Classification and Information Retrieval

2015

Xiaodong Liu, Jianfeng Gao, Xiaodong He, Li Deng, Kevin Duh, Ye-yi Wang. Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2015.

ReCoRD: Bridging the Gap between Human and Machine Commonsense Reading Comprehension

2018 · arXiv (Cornell University)

We present a large-scale dataset, ReCoRD, for machine reading comprehension requiring commonsense reasoning. Experiments on this dataset demonstrate that the performance of state-of-the-art MRC systems fall far behind human performance. ReCoRD represents a challenge for …

Curriculum Learning for Domain Adaptation in Neural Machine Translation

2019

Xuan Zhang, Pamela Shapiro, Gaurav Kumar, Paul McNamee, Marine Carpuat, Kevin Duh. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and …

Stochastic Answer Networks for Machine Reading Comprehension

2018

We propose a simple yet robust stochastic answer network (SAN) that simulates multi-step reasoning in machine reading comprehension. Compared to previous work such as ReasoNet which used reinforcement learning to determine the number of steps, …