9 citations · 23 across the 8 of their papers we have counts for
9 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…
Embedding-Enhanced Giza++: Improving Alignment in Low- and High- Resource Scenarios Using Embedding Space Geometry
Kelly Marchisio, Conghao Xiong, Philipp Koehn
A popular natural language processing task decades ago, word alignment has been dominated until recently by GIZA++, a statistical method based on the 30-year-old IBM models. New me…
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…
TICO-19: the Translation Initiative for Covid-19
Antonios Anastasopoulos, Alessandro Cattelan, Zi-Yi Dou +15
The COVID-19 pandemic is the worst pandemic to strike the world in over a century. Crucial to stemming the tide of the SARS-CoV-2 virus is communicating to vulnerable populations t…
Findings of the First Shared Task on Machine Translation Robustness
Xian Li, Paul Michel, Antonios Anastasopoulos +7
We share the findings of the first shared task on improving robustness of Machine Translation (MT). The task provides a testbed representing challenges facing MT models deployed in…