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20182026
most citedMonotonic Multihead Attention

68 citations · 234 across the 34 of their papers we have counts for

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Showing 2019 · cs.CLShow all

5 papers · 2 filters

cs.CL2019★ 25 cited

Harnessing Indirect Training Data for End-to-End Automatic Speech Translation: Tricks of the Trade

Juan Pino, Liezl Puzon, Jiatao Gu +3

For automatic speech translation (AST), end-to-end approaches are outperformed by cascaded models that transcribe with automatic speech recognition (ASR), then translate with machi…

cs.CL2019★ 68 cited

Monotonic Multihead Attention

Xutai Ma, Juan Pino, James Cross +2

Simultaneous machine translation models start generating a target sequence before they have encoded or read the source sequence. Recent approaches for this task either apply a fixe…

cs.CL2019★ 9 cited

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…

cs.CL2019★ 15 cited

On Evaluation of Adversarial Perturbations for Sequence-to-Sequence Models

Paul Michel, Xian Li, Graham Neubig +1

Adversarial examples --- perturbations to the input of a model that elicit large changes in the output --- have been shown to be an effective way of assessing the robustness of seq…

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…