9 citations · 17 across the 5 of their papers we have counts for
8 papers
Adapting High-resource NMT Models to Translate Low-resource Related Languages without Parallel Data
Wei-Jen Ko, Ahmed El-Kishky, Adithya Renduchintala +6
The scarcity of parallel data is a major obstacle for training high-quality machine translation systems for low-resource languages. Fortunately, some low-resource languages are lin…
Evaluating Saliency Methods for Neural Language Models
Shuoyang Ding, Philipp Koehn
Saliency methods are widely used to interpret neural network predictions, but different variants of saliency methods often disagree even on the interpretations of the same predicti…
XLEnt: Mining a Large Cross-lingual Entity Dataset with Lexical-Semantic-Phonetic Word Alignment
Ahmed El-Kishky, Adithya Renduchintala, James Cross +2
Cross-lingual named-entity lexica are an important resource to multilingual NLP tasks such as machine translation and cross-lingual wikification. While knowledge bases contain a la…
Learning Feature Weights using Reward Modeling for Denoising Parallel Corpora
Gaurav Kumar, Philipp Koehn, Sanjeev Khudanpur
Large web-crawled corpora represent an excellent resource for improving the performance of Neural Machine Translation (NMT) systems across several language pairs. However, since th…
Learning Policies for Multilingual Training of Neural Machine Translation Systems
Gaurav Kumar, Philipp Koehn, Sanjeev Khudanpur
Low-resource Multilingual Neural Machine Translation (MNMT) is typically tasked with improving the translation performance on one or more language pairs with the aid of high-resour…
Zero-Shot Cross-Lingual Dependency Parsing through Contextual Embedding Transformation
Haoran Xu, Philipp Koehn
Linear embedding transformation has been shown to be effective for zero-shot cross-lingual transfer tasks and achieve surprisingly promising results. However, cross-lingual embeddi…