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Found 17 papers, 5 papers with code
Tree-to-Sequence Attentional Neural Machine Translation
Most of the existing Neural Machine Translation (NMT) models focus on the conversion of sequential data and do not directly use syntactic information.
Domain Adaptation and Attention-Based Unknown Word Replacement in Chinese-to-Japanese Neural Machine Translation
This paper describes our UT-KAY system that participated in the Workshop on Asian Translation 2016.
Character-based Decoding in Tree-to-Sequence Attention-based Neural Machine Translation
This paper reports our systems (UT-AKY) submitted in the 3rd Workshop of Asian Translation 2016 (WAT{'}16) and their results in the English-to-Japanese translation task.
Learning to Parse and Translate Improves Neural Machine Translation
There has been relatively little attention to incorporating linguistic prior to neural machine translation.
Multilingual Extractive Reading Comprehension by Runtime Machine Translation
Given a target language without RC training data and a pivot language with RC training data (e. g. English), our method leverages existing RC resources in the pivot language by combining a competitive RC model in the pivot language with an attentive Neural Machine Translation (NMT) model.
Zero-Shot Cross-lingual Classification Using Multilingual Neural Machine Translation
However, little attention has been paid to leveraging representations learned by a multilingual NMT system to enable zero-shot multilinguality in other NLP tasks.
Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling
Lingvo is a Tensorflow framework offering a complete solution for collaborative deep learning research, with a particular focus towards sequence-to-sequence models.
Incorporating Source-Side Phrase Structures into Neural Machine Translation
In those NMT models, sentences are simply treated as sequences of words without any internal structure.
Combining Translation Memory with Neural Machine Translation
In this paper, we report our submission systems (geoduck) to the Timely Disclosure task on the 6th Workshop on Asian Translation (WAT) (Nakazawa et al., 2019).
Neural Text Generation with Artificial Negative Examples
In this paper, we propose to suppress an arbitrary type of errors by training the text generation model in a reinforcement learning framework, where we use a trainable reward function that is capable of discriminating between references and sentences containing the targeted type of errors.
XLM-T: Scaling up Multilingual Machine Translation with Pretrained Cross-lingual Transformer Encoders
Multilingual machine translation enables a single model to translate between different languages.
Improving Multilingual Translation by Representation and Gradient Regularization
At the gradient level, we leverage a small amount of direct data (in thousands of sentence pairs) to regularize model gradients.
Building Multilingual Machine Translation Systems That Serve Arbitrary X-Y Translations
Multilingual Neural Machine Translation (MNMT) enables one system to translate sentences from multiple source languages to multiple target languages, greatly reducing deployment costs compared with conventional bilingual systems.
Overview of the 9th Workshop on Asian Translation
This paper presents the results of the shared tasks from the 9th workshop on Asian translation (WAT2022).
Overview of the 8th Workshop on Asian Translation
This paper presents the results of the shared tasks from the 8th workshop on Asian translation (WAT2021).
Building Multilingual Machine Translation Systems That Serve Arbitrary XY Translations
Multilingual Neural Machine Translation (MNMT) enables one system to translate sentences from multiple source languages to multiple target languages, greatly reducing deployment costs compared with conventional bilingual systems.
