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Found 18 papers, 1 papers with code
TANDO: A Corpus for Document-level Machine Translation
Document-level Neural Machine Translation aims to increase the quality of neural translation models by taking into account contextual information.
Supervised and Unsupervised Minimalist Quality Estimators: Vicomtech's Participation in the WMT 2018 Quality Estimation Task
We describe Vicomtech{'}s participation in the WMT 2018 shared task on quality estimation, for which we submitted minimalist quality estimators.
STACC, OOV Density and N-gram Saturation: Vicomtech's Participation in the WMT 2018 Shared Task on Parallel Corpus Filtering
To address the specifics of the corpus filtering task, which features significant volumes of noisy data, the core method was expanded with a penalty based on the amount of unknown words in sentence pairs.
Weighted Set-Theoretic Alignment of Comparable Sentences
This article presents the STACCw system for the BUCC 2017 shared task on parallel sentence extraction from comparable corpora.
Machine Translation for Subtitling: A Large-Scale Evaluation
This article describes a large-scale evaluation of the use of Statistical Machine Translation for professional subtitling.
Exploiting a Large Strongly Comparable Corpus
The EITB corpus, a strongly comparable corpus in the news domain, is to be shared with the research community, as an aid for the development and testing of methods in comparable corpora exploitation, and as basis for the improvement of data-driven machine translation systems for this language pair.
ELRI: A Decentralised Network of National Relay Stations to Collect, Prepare and Share Language Resources
We describe the European Language Resource Infrastructure (ELRI), a decentralised network to help collect, prepare and share language resources.
To Case or not to case: Evaluating Casing Methods for Neural Machine Translation
We present a comparative evaluation of casing methods for Neural Machine Translation, to help establish an optimal pre- and post-processing methodology.
Handle with Care: A Case Study in Comparable Corpora Exploitation for Neural Machine Translation
We present the results of a case study in the exploitation of comparable corpora for Neural Machine Translation.
The Multilingual Anonymisation Toolkit for Public Administrations (MAPA) Project
We describe the MAPA project, funded under the Connecting Europe Facility programme, whose goal is the development of an open-source de-identification toolkit for all official European Union languages.
Online Learning over Time in Adaptive Neural Machine Translation
Adaptive Machine Translation purports to dynamically include user feedback to improve translation quality.
Split and Rephrase with Large Language Models
Our results provide a fine-grained analysis of the potential and limitations of large language models for SPRP, with significant improvements achievable using relatively small amounts of training data and model parameters overall, and remaining limitations for all models on the task.
Promoting Target Data in Context-aware Neural Machine Translation
Standard context-aware neural machine translation (NMT) typically relies on parallel document-level data, exploiting both source and target contexts.
