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Found 25 papers, 1 papers with code
On Machine Translation of User Reviews
This work investigates neural machine translation (NMT) systems for translating English user reviews into Croatian and Serbian, two similar morphologically complex languages.
Benefiting from Language Similarity in the Multilingual MT Training: Case Study of Indonesian and Malaysian
The development of machine translation (MT) has been successful in breaking the language barrier of the world’s top 10-20 languages.
Rakuten’s Participation in WAT 2022: Parallel Dataset Filtering by Leveraging Vocabulary Heterogeneity
This paper introduces our neural machine translation system’s participation in the WAT 2022 shared translation task (team ID: sakura).
Neural Machine Translation between similar South-Slavic languages
This paper describes the ADAPT-DCU machine translation systems built for the WMT 2020 shared task on Similar Language Translation.
Neural Machine Translation for translating into Croatian and Serbian
Furthermore, translation performance from English is much better than from German, partly because German is morphologically more complex and partly because the corpus consists mostly of parallel human translations instead of original text and its human translation.
Using Multiple Subwords to Improve English-Esperanto Automated Literary Translation Quality
As an additional contribution, we made available a set of English-Esperanto parallel data in the literary domain.
The Impact of Indirect Machine Translation on Sentiment Classification
Furthermore, as performing a direct translation is not always possible, we explore the performance of automatic classifiers on sentences that have been translated using a pivot MT system.
Selecting Backtranslated Data from Multiple Sources for Improved Neural Machine Translation
Machine translation (MT) has benefited from using synthetic training data originating from translating monolingual corpora, a technique known as backtranslation.
Facilitating Access to Multilingual COVID-19 Information via Neural Machine Translation
Every day, more people are becoming infected and dying from exposure to COVID-19.
Multiple Segmentations of Thai Sentences for Neural Machine Translation
Thai is a low-resource language, so it is often the case that data is not available in sufficient quantities to train an Neural Machine Translation (NMT) model which perform to a high level of quality.
A Tool for Facilitating OCR Postediting in Historical Documents
This paper reports on a tool built for postediting the output of Tesseract, more specifically for correcting common errors in digitized historical documents.
Selecting Artificially-Generated Sentences for Fine-Tuning Neural Machine Translation
Neural Machine Translation (NMT) models tend to achieve best performance when larger sets of parallel sentences are provided for training.
Combining SMT and NMT Back-Translated Data for Efficient NMT
Neural Machine Translation (NMT) models achieve their best performance when large sets of parallel data are used for training.
Combining PBSMT and NMT Back-translated Data for Efficient NMT
Neural Machine Translation (NMT) models achieve their best performance when large sets of parallel data are used for training.
Transductive Data-Selection Algorithms for Fine-Tuning Neural Machine Translation
Machine Translation models are trained to translate a variety of documents from one language into another.
Adaptation of Machine Translation Models with Back-translated Data using Transductive Data Selection Methods
These methods ensure that selected sentences share n-grams with the test set so the NMT model can be adapted to translate it.
ABI Neural Ensemble Model for Gender Prediction Adapt Bar-Ilan Submission for the CLIN29 Shared Task on Gender Prediction
In contradiction with the results described in previous comparable shared tasks, our neural models performed better than our best traditional approaches with our best feature set-up.
The ADAPT System Description for the IWSLT 2018 Basque to English Translation Task
In this paper we present the ADAPT system built for the Basque to English Low Resource MT Evaluation Campaign.
Data Selection with Feature Decay Algorithms Using an Approximated Target Side
A limitation of these methods to date is that using the source-side test set does not by itself guarantee that sentences are selected with correct translations, or translations that are suitable given the test-set domain.
Extracting In-domain Training Corpora for Neural Machine Translation Using Data Selection Methods
Data selection is a process used in selecting a subset of parallel data for the training of machine translation (MT) systems, so that 1) resources for training might be reduced, 2) trained models could perform better than those trained with the whole corpus, and/or 3) trained models are more tailored to specific domains.
Understanding Meanings in Multilingual Customer Feedback
Understanding and being able to react to customer feedback is the most fundamental task in providing good customer service.
Investigating Backtranslation in Neural Machine Translation
A prerequisite for training corpus-based machine translation (MT) systems -- either Statistical MT (SMT) or Neural MT (NMT) -- is the availability of high-quality parallel data.
IJCNLP-2017 Task 4: Customer Feedback Analysis
This document introduces the IJCNLP 2017 Shared Task on Customer Feedback Analysis.
ADAPT Centre Cone Team at IJCNLP-2017 Task 5: A Similarity-Based Logistic Regression Approach to Multi-choice Question Answering in an Examinations Shared Task
We describe the work of a team from the ADAPT Centre in Ireland in addressing automatic answer selection for the Multi-choice Question Answering in Examinations shared task.
