# Enriching Word Vectors with Subword Information

A vector representation is associated to each character $n$-gram; words being represented as the sum of these representations.

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# Fully Character-Level Neural Machine Translation without Explicit Segmentation

We observe that on CS-EN, FI-EN and RU-EN, the quality of the multilingual character-level translation even surpasses the models specifically trained on that language pair alone, both in terms of BLEU score and human judgment.

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# Context Gates for Neural Machine Translation

In neural machine translation (NMT), generation of a target word depends on both source and target contexts.

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# Learning Distributed Representations of Texts and Entities from Knowledge Base

Given a text in the KB, we train our proposed model to predict entities that are relevant to the text.

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# Replicability Analysis for Natural Language Processing: Testing Significance with Multiple Datasets

With the ever-growing amounts of textual data from a large variety of languages, domains, and genres, it has become standard to evaluate NLP algorithms on multiple datasets in order to ensure consistent performance across heterogeneous setups.

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# Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation

In addition to improving the translation quality of language pairs that the model was trained with, our models can also learn to perform implicit bridging between language pairs never seen explicitly during training, showing that transfer learning and zero-shot translation is possible for neural translation.

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# Ordinal Common-sense Inference

Humans have the capacity to draw common-sense inferences from natural language: various things that are likely but not certain to hold based on established discourse, and are rarely stated explicitly.

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