English-German

The Edinburgh submissions to the shared task of the Third Workshop on Statistical Machine Translation (WMT-2008) incorporate recent advances to the open source Moses system. We made a special effort …...

We describe our hybrid machine trans- lation system which has been developed for and used in the WMT10 shared task. We compute translations from a rule- based MT system and …...

This paper explores a number of simple and effective techniques to adapt statisti- cal machine translation (SMT) systems in the medical domain. Comparative exper- iments are conducted on large corpora …...

This paper describes the string-to-tree sys- tems built at the University of Edin- burgh for the WMT 2014 shared trans- lation task. We developed systems for English-German, Czech-English, French- English, …...

We describe Stanford’s participation in the French-English and English-German tracks of the 2014 Workshop on Statisti- cal Machine Translation (WMT). Our sys- tems used large feature sets, word classes, and …...

Within the field of Statistical Machine Trans- lation (SMT), the neural approach (NMT) has recently emerged as the first technology able to challenge the long-standing dominance of phrase-based approaches (PBMT). …...

AbstractSpoken Language Translation is currently a hot topic in the research community. This task is very complex, involving automatic speech recognition, text-normalization and machine translation. We present our speech translation …...

AbstractBilingual sequence models improve phrase-based translation and reordering by overcoming phrasal independence assumption and handling long range reordering. However, due to data sparsity, these models often fall back to very …...

This paper describes the NICT-NAIST system for the WMT 2017 shared multi- modal machine translation task for both language pairs, English-to-German and English-to-French. We built a hierarchical phrase-based (Hiero) translation …...

This paper presents the contribution of the Unbabel team to the WMT 2017 Shared Task on Translation Quality Estimation. We participated on the word-level and sentence-level tracks. We describe our …...

This paper describes the Neural Machine Translation systems of Xiamen University for the translation tasks of WMT 17. Our systems are based on the Encoder-Decoder framework with attention. We partici- …...

Training models for the automatic correction of machine-translated text usually relies on data consisting of (source, MT, human post- edit) triplets providing, for each source sentence, examples of translation errors …...

AbstractWe investigate adaptive ensemble weighting for Neural Machine Translation, addressing the case of improving performance on a new and potentially unknown domain without sacrificing performance on the original domain. We …...

AbstractDespite the progress made in sentence-level NMT, current systems still fall short at achieving fluent, good quality translation for a full document. Recent works in context-aware NMT consider only a …...

AbstractRecent machine translation shared tasks have shown top-performing systems to tie or in some cases even outperform human translation. Such conclusions about system and human performance are, however, based on …...

Constrained decoding forces a certain set words or phrases to appear in the translation results and is very useful when adapting MT to a certain domain. In recent years, the …...

AbstractThis paper investigates the use of bilingual word embeddings for mining Hiligaynon translations of English words. There is very little research on Hiligaynon, an extremely low-resource language of Malayo-Polynesian origin …...

AbstractMachine translation of scientific abstracts and terminologies has the potential to support health professionals and biomedical researchers in some of their activities. In the fifth edition of the WMT Biomedical …...

AbstractWe present the results of the 6th round of the WMT task on MT Automatic Post-Editing. The task consists in automatically correcting the output of a “black-box” machine translation system …...

AbstractWe report the results of the first edition of the WMT shared task on chat translation. The task consisted of translating bilingual conversational text, in particular customer support chats for …...

AbstractThe quality of machine translation systems has dramatically improved over the last decade, and as a result, evaluation has become an increasingly challenging problem. This paper describes our contribution to …...

AbstractThis paper describes the NICT Kyoto submission for the WMT’20 Quality Estimation (QE) shared task. We participated in Task 2: Word and Sentence-level Post-editing Effort, which involved Wikipedia data and …...

AbstractContext-aware neural machine translation (NMT) incorporates contextual information of surrounding texts, that can improve the translation quality of document-level machine translation. Many existing works on context-aware NMT have focused on …...

AbstractFine-grained control of machine translation (MT) outputs along multiple attributes is critical for many modern MT applications and is a requirement for gaining users’ trust. A standard approach for exerting …...

AbstractThis paper presents the results of the WMT21 Metrics Shared Task. Participants were asked to score the outputs of the translation systems competing in the WMT21 News Translation Task with …...

AbstractIn this paper and we identify an interesting kind of error in the output of Unsupervised Neural Machine Translation (UNMT) systems like Undreamt1. We refer to this error type as …...

AbstractThis paper describes Amazon Alexa AI’s submission to the IWSLT 2022 Offline Speech Translation Task. Our system is an end-to-end speech translation model that leverages pretrained models and cross modality …...

AbstractThis paper presents the BJTU-Toshiba joint submission for WMT 2022 quality estimation shared task. We only participate in Task 1 (quality prediction) of the shared task, focusing on the sentence-level …...

AbstractThe PROMT systems are trained with the MarianNMT toolkit. All systems use the transformer-big configuration. We use BPE for text encoding, the vocabulary sizes vary from 24k to 32k for …...

AbstractWe explore the use of source factors in context-aware neural machine translation, specifically concatenation-based models, to improve the translation quality of inter-sentential phenomena. Context sentences are typically concatenated to the …...

AbstractThis paper presents HW-TSC’s submissions to the IWSLT 2023 Offline Speech Translation task, including speech translation of talks from English to German, Chinese, and Japanese, respectively. We participate in all …...

AbstractProgress in document-level Machine Translation is hindered by the lack of parallel training data that include context information. In this work, we evaluate the potential of data augmentation techniques to …...

AbstractIn this work, we study the robustness of two typical terminology translation methods: Placeholder (PH) and Code-Switch (CS), concerning (1) the number of constraints and (2) the target constraint length. …...