An Arabic-Hebrew parallel corpus of TED talks

By Mauro Cettolo. 2016

12 citations recorded by the source corpus.

We describe an Arabic-Hebrew parallel corpus of TED talks built upon WIT3, the Web inventory that repurposes the original content of the TED website in a way which is more convenient for MT researchers. The benchmark consists of about 2,000 talks, whose subtitles in Arabic and Hebrew have been accurately aligned and rearranged in sentences, for a total of about 3.5M tokens per language. Talks have been partitioned in train, development and test sets similarly in all respects to the MT tasks of the IWSLT 2016 evaluation campaign. In addition to describing the benchmark, we list the problems encountered in preparing it and the novel methods designed to solve them. Baseline MT results and some measures on sentence length are provided as an extrinsic evaluation of the quality of the benchmark.

Subfields: Artificial Intelligence, Natural Language Processing, Machine Translation

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Citations in the corpus

What do Neural Machine Translation Models Learn about Morphology? (2017, methodology): “Our translation models are trained on the WIT3 corpus of TED talks (Cettolo et al., 2012; Cettolo, 2016) made available for IWSLT 2016.”
A Survey of the Usages of Deep Learning for Natural Language Processing (2020, methodology): “Encoders and decoders were LSTM-based models (some with attention mechanisms) or character aware CNNs, and the models were trained on the WIT 3 corpus [83], [84].”
On internal language representations in deep learning: an analysis of machine translation and speech recognition (2018, methodology): “MT data The dataset used for training machine translation models is the WIT3 corpus of TED talks [55, 56] made available for IWSLT 2016.”
Understanding and Improving Morphological Learning in the Neural Machine Translation Decoder (2017, methodology): “We used the German-English and Czech-English datasets from the WIT 3 TED corpus (Cettolo, 2016)”