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Found 33 papers, 9 papers with code
Samsung and University of Edinburgh’s System for the IWSLT 2019
This paper describes the joint submission to the IWSLT 2019 English to Czech task by Samsung RD Institute, Poland, and the University of Edinburgh.
Findings of the IWSLT 2022 Evaluation Campaign
The evaluation campaign of the 19th International Conference on Spoken Language Translation featured eight shared tasks: (i) Simultaneous speech translation, (ii) Offline speech translation, (iii) Speech to speech translation, (iv) Low-resource speech translation, (v) Multilingual speech translation, (vi) Dialect speech translation, (vii) Formality control for speech translation, (viii) Isometric speech translation.
Speed-optimized, Compact Student Models that Distill Knowledge from a Larger Teacher Model: the UEDIN-CUNI Submission to the WMT 2020 News Translation Task
We describe the joint submission of the University of Edinburgh and Charles University, Prague, to the Czech/English track in the WMT 2020 Shared Task on News Translation.
Efficient Machine Translation with Model Pruning and Quantization
We participated in all tracks of the WMT 2021 efficient machine translation task: single-core CPU, multi-core CPU, and GPU hardware with throughput and latency conditions.
Findings of the WMT 2021 Shared Task on Efficient Translation
The machine translation efficiency task challenges participants to make their systems faster and smaller with minimal impact on translation quality.
Findings of the 2021 Conference on Machine Translation (WMT21)
This paper presents the results of the newstranslation task, the multilingual low-resourcetranslation for Indo-European languages, thetriangular translation task, and the automaticpost-editing task organised as part of the Con-ference on Machine Translation (WMT) 2021. In the news task, participants were asked tobuild machine translation systems for any of10 language pairs, to be evaluated on test setsconsisting mainly of news stories.
On Instruction-Finetuning Neural Machine Translation Models
In this work, we introduce instruction finetuning for Neural Machine Translation (NMT) models, which distills instruction following capabilities from Large Language Models (LLMs) into orders-of-magnitude smaller NMT models.
PyMarian: Fast Neural Machine Translation and Evaluation in Python
We describe a Python interface to Marian NMT, a C++-based training and inference toolkit for sequence-to-sequence models, focusing on machine translation.
Preliminary WMT24 Ranking of General MT Systems and LLMs
This is the preliminary ranking of WMT24 General MT systems based on automatic metrics.
Error Span Annotation: A Balanced Approach for Human Evaluation of Machine Translation
High-quality Machine Translation (MT) evaluation relies heavily on human judgments.
SOTASTREAM: A Streaming Approach to Machine Translation Training
Many machine translation toolkits make use of a data preparation step wherein raw data is transformed into a tensor format that can be used directly by the trainer.
To Ship or Not to Ship: An Extensive Evaluation of Automatic Metrics for Machine Translation
Automatic metrics are commonly used as the exclusive tool for declaring the superiority of one machine translation system's quality over another.
On User Interfaces for Large-Scale Document-Level Human Evaluation of Machine Translation Outputs
Recent studies emphasize the need of document context in human evaluation of machine translations, but little research has been done on the impact of user interfaces on annotator productivity and the reliability of assessments.
A Crash Course in Automatic Grammatical Error Correction
Grammatical Error Correction (GEC) is the task of automatically detecting and correcting all types of errors in written text.
Findings of the 2020 Conference on Machine Translation (WMT20)
In the news task, participants were asked to build machine translation systems for any of 11 language pairs, to be evaluated on test sets consisting mainly of news stories.
Edinburgh's Submissions to the 2020 Machine Translation Efficiency Task
We participated in all tracks of the Workshop on Neural Generation and Translation 2020 Efficiency Shared Task: single-core CPU, multi-core CPU, and GPU.
Minimally-Augmented Grammatical Error Correction
There has been an increased interest in low-resource approaches to automatic grammatical error correction.
From Research to Production and Back: Ludicrously Fast Neural Machine Translation
Taking our dominating submissions to the previous edition of the shared task as a starting point, we develop improved teacher-student training via multi-agent dual-learning and noisy backward-forward translation for Transformer-based student models.
Neural Grammatical Error Correction Systems with Unsupervised Pre-training on Synthetic Data
Considerable effort has been made to address the data sparsity problem in neural grammatical error correction.
Ranked #16 on
Grammatical Error Correction
on BEA-2019 (test)
The University of Edinburgh's Submissions to the WMT19 News Translation Task
For all translation directions, we created or used back-translations of monolingual data in the target language as additional synthetic training data.
The University of Edinburgh's Submissions to the WMT18 News Translation Task
The University of Edinburgh made submissions to all 14 language pairs in the news translation task, with strong performances in most pairs.
MS-UEdin Submission to the WMT2018 APE Shared Task: Dual-Source Transformer for Automatic Post-Editing
This paper describes the Microsoft and University of Edinburgh submission to the Automatic Post-editing shared task at WMT2018.
Neural Machine Translation Techniques for Named Entity Transliteration
Transliterating named entities from one language into another can be approached as neural machine translation (NMT) problem, for which we use deep attentional RNN encoder-decoder models.
Marian: Cost-effective High-Quality Neural Machine Translation in C++
This paper describes the submissions of the "Marian" team to the WNMT 2018 shared task.
Approaching Neural Grammatical Error Correction as a Low-Resource Machine Translation Task
Previously, neural methods in grammatical error correction (GEC) did not reach state-of-the-art results compared to phrase-based statistical machine translation (SMT) baselines.
Ranked #1 on
Grammatical Error Correction
on _Restricted_
Near Human-Level Performance in Grammatical Error Correction with Hybrid Machine Translation
We combine two of the most popular approaches to automated Grammatical Error Correction (GEC): GEC based on Statistical Machine Translation (SMT) and GEC based on Neural Machine Translation (NMT).
Marian: Fast Neural Machine Translation in C++
We present Marian, an efficient and self-contained Neural Machine Translation framework with an integrated automatic differentiation engine based on dynamic computation graphs.
An Exploration of Neural Sequence-to-Sequence Architectures for Automatic Post-Editing
In this work, we explore multiple neural architectures adapted for the task of automatic post-editing of machine translation output.
Pushing the Limits of Translation Quality Estimation
Translation quality estimation is a task of growing importance in NLP, due to its potential to reduce post-editing human effort in disruptive ways.
Phrase-based Machine Translation is State-of-the-Art for Automatic Grammatical Error Correction
In this work, we study parameter tuning towards the M^2 metric, the standard metric for automatic grammar error correction (GEC) tasks.
Log-linear Combinations of Monolingual and Bilingual Neural Machine Translation Models for Automatic Post-Editing
This paper describes the submission of the AMU (Adam Mickiewicz University) team to the Automatic Post-Editing (APE) task of WMT 2016.
