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An Empirical Exploration of Curriculum Learning for Neural Machine Translation
Xuan Zhang, Gaurav Kumar, Huda Khayrallah +6
Machine translation systems based on deep neural networks are expensive to train. Curriculum learning aims to address this issue by choosing the order in which samples are presente…
ReCoRD: Bridging the Gap between Human and Machine Commonsense Reading Comprehension
Sheng Zhang, Xiaodong Liu, Jingjing Liu +3
We present a large-scale dataset, ReCoRD, for machine reading comprehension requiring commonsense reasoning. Experiments on this dataset demonstrate that the performance of state-o…
Stochastic Answer Networks for SQuAD 2.0
Xiaodong Liu, Wei Li, Yuwei Fang +3
This paper presents an extension of the Stochastic Answer Network (SAN), one of the state-of-the-art machine reading comprehension models, to be able to judge whether a question is…
BPE and CharCNNs for Translation of Morphology: A Cross-Lingual Comparison and Analysis
Pamela Shapiro, Kevin Duh
Neural Machine Translation (NMT) in low-resource settings and of morphologically rich languages is made difficult in part by data sparsity of vocabulary words. Several methods have…
Freezing Subnetworks to Analyze Domain Adaptation in Neural Machine Translation
Brian Thompson, Huda Khayrallah, Antonios Anastasopoulos +7
To better understand the effectiveness of continued training, we analyze the major components of a neural machine translation system (the encoder, decoder, and each embedding space…
Character-Aware Decoder for Translation into Morphologically Rich Languages
Adithya Renduchintala, Pamela Shapiro, Kevin Duh +1
Neural machine translation (NMT) systems operate primarily on words (or sub-words), ignoring lower-level patterns of morphology. We present a character-aware decoder designed to ca…
How Do Source-side Monolingual Word Embeddings Impact Neural Machine Translation?
Shuoyang Ding, Kevin Duh
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 (N…
Halo: Learning Semantics-Aware Representations for Cross-Lingual Information Extraction
Hongyuan Mei, Sheng Zhang, Kevin Duh +1
Cross-lingual information extraction (CLIE) is an important and challenging task, especially in low resource scenarios. To tackle this challenge, we propose a training method, call…
Cross-lingual Semantic Parsing
Sheng Zhang, Kevin Duh, Benjamin Van Durme
We introduce the task of cross-lingual semantic parsing: mapping content provided in a source language into a meaning representation based on a target language. We present: (1) a m…
Fine-grained Entity Typing through Increased Discourse Context and Adaptive Classification Thresholds
Sheng Zhang, Kevin Duh, Benjamin Van Durme
Fine-grained entity typing is the task of assigning fine-grained semantic types to entity mentions. We propose a neural architecture which learns a distributional semantic represen…
Stochastic Answer Networks for Natural Language Inference
Xiaodong Liu, Kevin Duh, Jianfeng Gao
We propose a stochastic answer network (SAN) to explore multi-step inference strategies in Natural Language Inference. Rather than directly predicting the results given the inputs,…