Machine translation is the task of translating a sentence in a source language to a different target language
( Image credit: Google seq2seq )
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We are able to demonstrate the adversary's high success rate of attacks, while maintaining functionality for regular users, with triggers inconspicuous by the human administrators.
Human evaluation of modern high-quality machine translation systems is a difficult problem, and there is increasing evidence that inadequate evaluation procedures can lead to erroneous conclusions.
We take the first step to address the task of automatically generating shellcodes, i. e., small pieces of code used as a payload in the exploitation of a software vulnerability, starting from natural language comments.
In this paper, we present a novel approach for calculating the valence (the positivity or negativity of the perceived emotion) of a chord progression within a lead sheet, using pre-defined mood tags proposed by music experts.
We demonstrate empirically that a large English language model coupled with modern machine translation outperforms native language models in most Scandinavian languages.
We collect data from open sources on the Internet, and classify them into different categories, each labeled with a specific language style 3.
Ranked #1 on Machine Translation on IWSLT2015 English-Vietnamese (using extra training data)
In image captioning, we train a multi-tasking machine translation and image captioning pipeline for Arabic and English from which the Arabic training data is a wikily translation of the English captioning data.
Results show that the reward optimization with BLEURT is able to increase the metric scores by a large margin, in contrast to limited gain when training with smoothed BLEU.
We propose Future Discriminators for Generation (FUDGE), a flexible and modular method for controlled text generation.