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20162024
most citedNo Language Left Behind: Scaling Human-Centered Machine Translation

373 citations · 842 across the 24 of their papers we have counts for

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Showing 2019Show all

9 papers · 1 filter

cs.CL2019★ 1 cited

Machine Translation Evaluation Meets Community Question Answering

Francisco Guzmán, Lluís Màrquez, Preslav Nakov

We explore the applicability of machine translation evaluation (MTE) methods to a very different problem: answer ranking in community Question Answering. In particular, we adopt a…

cs.CL2019

Pairwise Neural Machine Translation Evaluation

Francisco Guzman, Shafiq Joty, Lluis Marquez +1

We present a novel framework for machine translation evaluation using neural networks in a pairwise setting, where the goal is to select the better translation from a pair of hypot…

cs.CL2019

DiscoTK: Using Discourse Structure for Machine Translation Evaluation

Shafiq Joty, Francisco Guzman, Lluis Marquez +1

We present novel automatic metrics for machine translation evaluation that use discourse structure and convolution kernels to compare the discourse tree of an automatic translation…

cs.CL2019★ 246 cited

CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data

Guillaume Wenzek, Marie-Anne Lachaux, Alexis Conneau +4

Pre-training text representations have led to significant improvements in many areas of natural language processing. The quality of these models benefits greatly from the size of t…

cs.CL2019

CCAligned: A Massive Collection of Cross-Lingual Web-Document Pairs

Ahmed El-Kishky, Vishrav Chaudhary, Francisco Guzman +1

Cross-lingual document alignment aims to identify pairs of documents in two distinct languages that are of comparable content or translations of each other. In this paper, we explo…

cs.CL2019

Unsupervised Cross-lingual Representation Learning at Scale

Alexis Conneau, Kartikay Khandelwal, Naman Goyal +7

This paper shows that pretraining multilingual language models at scale leads to significant performance gains for a wide range of cross-lingual transfer tasks. We train a Transfor…

cs.CL2019★ 67 cited

WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia

Holger Schwenk, Vishrav Chaudhary, Shuo Sun +2

We present an approach based on multilingual sentence embeddings to automatically extract parallel sentences from the content of Wikipedia articles in 85 languages, including sever…

cs.CL2019

Low-Resource Corpus Filtering using Multilingual Sentence Embeddings

Vishrav Chaudhary, Yuqing Tang, Francisco Guzmán +2

In this paper, we describe our submission to the WMT19 low-resource parallel corpus filtering shared task. Our main approach is based on the LASER toolkit (Language-Agnostic SEnten…

cs.CL2019

The FLoRes Evaluation Datasets for Low-Resource Machine Translation: Nepali-English and Sinhala-English

Francisco Guzmán, Peng-Jen Chen, Myle Ott +5

For machine translation, a vast majority of language pairs in the world are considered low-resource because they have little parallel data available. Besides the technical challeng…