3260 papers • 126 benchmarks • 313 datasets
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An architecture to learn joint multilingual sentence representations for 93 languages, belonging to more than 30 different families and written in 28 different scripts using a single BiLSTM encoder with a shared byte-pair encoding vocabulary for all languages, coupled with an auxiliary decoder and trained on publicly available parallel corpora.
This paper uses the framework of neural machine translation to learn joint sentence representations across six very different languages, and provides experimental evidence that sentences that are close in embedding space are indeed semantically highly related, but often have quite different structure and syntax.
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