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Found 41 papers, 12 papers with code
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.
On-the-Fly Fusion of Large Language Models and Machine Translation
We propose the on-the-fly ensembling of a machine translation model with an LLM, prompted on the same task and input.
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.
Escaping the sentence-level paradigm in machine translation
It is well-known that document context is vital for resolving a range of translation ambiguities, and in fact the document setting is the most natural setting for nearly all translation.
The JHU-Microsoft Submission for WMT21 Quality Estimation Shared Task
This paper presents the JHU-Microsoft joint submission for WMT 2021 quality estimation shared task.
Levenshtein Training for Word-level Quality Estimation
We propose a novel scheme to use the Levenshtein Transformer to perform the task of word-level quality estimation.
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.
The Curious Case of Hallucinations in Neural Machine Translation
In this work, we study hallucinations in Neural Machine Translation (NMT), which lie at an extreme end on the spectrum of NMT pathologies.
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)
Microsoft Translator at WMT 2019: Towards Large-Scale Document-Level Neural Machine Translation
Using document boundaries present in the authentic and synthetic parallel data, we create sequences of up to 1000 subword segments and train transformer translation models.
Dual Conditional Cross-Entropy Filtering of Noisy Parallel Corpora
For each sentence pair of the noisy parallel corpus we compute cross-entropy scores according to two inverse translation models trained on clean data.
Microsoft's Submission to the WMT2018 News Translation Task: How I Learned to Stop Worrying and Love the Data
This paper describes the Microsoft submission to the WMT2018 news translation shared task.
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.
Accelerating Asynchronous Stochastic Gradient Descent for Neural Machine Translation
In order to extract the best possible performance from asynchronous stochastic gradient descent one must increase the mini-batch size and scale the learning rate accordingly.
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.
Achieving Human Parity on Automatic Chinese to English News Translation
Machine translation has made rapid advances in recent years.
Ranked #3 on
Machine Translation
on WMT 2017 English-Chinese
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.
The SUMMA Platform Prototype
We present the first prototype of the SUMMA Platform: an integrated platform for multilingual media monitoring.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
Nematus: a Toolkit for Neural Machine Translation
We present Nematus, a toolkit for Neural Machine Translation.
Predicting Target Language CCG Supertags Improves Neural Machine Translation
Our results on WMT data show that explicitly modeling target-syntax improves machine translation quality for German->English, a high-resource pair, and for Romanian->English, a low-resource pair and also several syntactic phenomena including prepositional phrase attachment.
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.
Fast, Scalable Phrase-Based SMT Decoding
The utilization of statistical machine translation (SMT) has grown enormously over the last decade, many using open-source software developed by the NLP community.
Is Neural Machine Translation Ready for Deployment? A Case Study on 30 Translation Directions
In this paper we provide the largest published comparison of translation quality for phrase-based SMT and neural machine translation across 30 translation directions.
Target-Side Context for Discriminative Models in Statistical Machine Translation
Discriminative translation models utilizing source context have been shown to help statistical machine translation performance.
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.
The AMU-UEDIN Submission to the WMT16 News Translation Task: Attention-based NMT Models as Feature Functions in Phrase-based SMT
For the Russian-English task, our submission achieves the top BLEU result, outperforming the best pure neural system by 1. 1 BLEU points and our own phrase-based baseline by 1. 6 BLEU.
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.
The United Nations Parallel Corpus v1.0
This paper describes the creation process and statistics of the official United Nations Parallel Corpus, the first parallel corpus composed from United Nations documents published by the original data creator.
