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Found 10 papers, 1 papers with code
MT-GenEval: A Counterfactual and Contextual Dataset for Evaluating Gender Accuracy in Machine Translation
As generic machine translation (MT) quality has improved, the need for targeted benchmarks that explore fine-grained aspects of quality has increased.
A baseline revisited: Pushing the limits of multi-segment models for context-aware translation
This paper addresses the task of contextual translation using multi-segment models.
Joint translation and unit conversion for end-to-end localization
A variety of natural language tasks require processing of textual data which contains a mix of natural language and formal languages such as mathematical expressions.
Neural Machine Translation for Cross-Lingual Pronoun Prediction
In this paper we present our systems for the DiscoMT 2017 cross-lingual pronoun prediction shared task.
Does Neural Machine Translation Benefit from Larger Context?
We propose a neural machine translation architecture that models the surrounding text in addition to the source sentence.
Document Neural Autoregressive Distribution Estimation
We present an approach based on feed-forward neural networks for learning the distribution of textual documents.
An Autoencoder Approach to Learning Bilingual Word Representations
Cross-language learning allows us to use training data from one language to build models for a different language.
Learning Multilingual Word Representations using a Bag-of-Words Autoencoder
Recent work on learning multilingual word representations usually relies on the use of word-level alignements (e. g. infered with the help of GIZA++) between translated sentences, in order to align the word embeddings in different languages.
A Neural Autoregressive Topic Model
We describe a new model for learning meaningful representations of text documents from an unlabeled collection of documents.
