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Found 10 papers, 1 papers with code
Document-level Neural MT: A Systematic Comparison
In this paper we provide a systematic comparison of existing and new document-level neural machine translation solutions.
Findings of the WMT 2020 Shared Task on Chat Translation
We report the results of the first edition of the WMT shared task on chat translation.
FBK’s Neural Machine Translation Systems for IWSLT 2016
They leverage linguistic information such as lemmas and part-of-speech tags of the source words in the form of additional factors along with the words.
Unbabel's Participation in the WMT19 Translation Quality Estimation Shared Task
We present the contribution of the Unbabel team to the WMT 2019 Shared Task on Quality Estimation.
Unbabel's Submission to the WMT2019 APE Shared Task: BERT-based Encoder-Decoder for Automatic Post-Editing
Analogously to dual-encoder architectures we develop a BERT-based encoder-decoder (BED) model in which a single pretrained BERT encoder receives both the source src and machine translation tgt strings.
Neural vs. Phrase-Based Machine Translation in a Multi-Domain Scenario
State-of-the-art neural machine translation (NMT) systems are generally trained on specific domains by carefully selecting the training sets and applying proper domain adaptation techniques.
WAGS: A Beautiful English-Italian Benchmark Supporting Word Alignment Evaluation on Rare Words
This paper presents WAGS (Word Alignment Gold Standard), a novel benchmark which allows extensive evaluation of WA tools on out-of-vocabulary (OOV) and rare words.
