Findings of the 2015 Workshop on Statistical Machine Translation

This paper presents the results of the WMT15 shared tasks, which included a standard news translation task, a metrics task, a tuning task, a task for run-time estimation of machine translation quality, and an automatic post-editing task. This year, 68 machine translation systems from 24 institutions were submitted to the ten translation directions in the standard translation task. An additional 7 anonymized systems were included, and were then evaluated both automatically and manually. The quality estimation task had three subtasks, with a total of 10 teams, submitting 34 entries. The pilot automatic postediting task had a total of 4 teams, submitting 7 entries

Findings of the 2014 Workshop on Statistical Machine Translation

This paper presents the results of the WMT14 shared tasks, which included a standard news translation task, a separate medical translation task, a task for run-time estimation of machine translation quality, and a metrics task. This year, 143 machine translation systems from 23 institutions were submitted to the ten translation directions in the standard translation task. An additional 6 anonymized systems were included, and were then evaluated both automatically and manually. The quality estimation task had four subtasks, with a total of 10 teams, submitting 57 entries

Findings of the 2016 Conference on Machine Translation.

This paper presents the results of the WMT16 shared tasks, which included five machine translation (MT) tasks (standard news, IT-domain, biomedical, multimodal, pronoun), three evaluation tasks (metrics, tuning, run-time estimation of MT quality), and an automatic post-editing task and bilingual document alignment task. This year, 102 MT systems from 24 institutions (plus 36 anonymized online systems) were submitted to the 12 translation directions in the news translation task. The IT-domain task received 31 submissions from 12 institutions in 7 directions and the Biomedical task received 15 submissions systems from 5 institutions. Evaluation was both automatic and manual (relative ranking and 100-point scale assessments). The quality estimation task had three subtasks, with a total of 14 teams, submitting 39 entries. The automatic post-editing task had a total of 6 teams, submitting 11 entries

Findings of the 2016 Conference on Machine Translation (WMT16)

This paper presents the results of the WMT16 shared tasks, which included five machine translation (MT) tasks (standard news, IT-domain, biomedical, multimodal, pronoun), three evaluation tasks (metrics, tuning, run-time estimation of MT quality), and an automatic post-editing task and bilingual document alignment task. This year, 102 MT systems from 24 institutions (plus 36 anonymized online systems) were submitted to the 12 translation directions in the news translation task. The IT-domain task received 31 submissions from 12 institutions in 7 directions and the Biomedical task received 15 submissions systems from 5 institutions. Evaluation was both automatic and manual (relative ranking and 100-point scale assessments)

UGENT-LT3 SCATE system for machine translation quality estimation

This paper describes the submission of the UGENT-LT3 SCATE system to the WMT15 Shared Task on Quality Estima-tion (QE), viz. English-Spanish word and sentence-level QE. We conceived QE as a supervised Machine Learning (ML) problem and designed additional features and combined these with the baseline feature set to estimate quality. The sen-tence-level QE system re-uses the word level predictions of the word-level QE system. We experimented with different learning methods and observe improve-ments over the baseline system for word-level QE with the use of the new features and by combining learning methods into ensembles. For sentence-level QE we show that using a single feature based on word-level predictions can perform better than the baseline system and using this in combination with additional features led to further improvements in performance

UGENT-LT3 SCATE Submission for WMT16 Shared Task on Quality Estimation

This paper describes the submission of the UGENT-LT3 SCATE system to the WMT16 Shared Task on Quality Estimation (QE), viz. English-German word and sentence-level QE. Based on the observation that the data set is homogeneous (all sentences belong to the IT domain), we performed bilingual terminology extraction and added features derived from the resulting term list to the well-performing features of the word-level QE task of last year. For sentence-level QE, we analyzed the importance of the features and based on those insights extended the feature set of last year. We also experimented with different learning methods and ensembles. We present our observations from the different experiments we conducted and our submissions for both tasks

An Unsupervised Method for Automatic Translation Memory Cleaning.

We address the problem of automatically cleaning a large-scale Translation Memory (TM) in a fully unsupervised fashion, i.e. without human-labelled data. We approach the task by: i) designing a set of features that capture the similarity between two text segments in different languages, ii) use them to induce reliable training labels for a subset of the translation units (TUs) contained in the TM, and iii) use the automatically labelled data to train an ensemble of binary classifiers. We apply our method to clean a test set composed of 1,000 TUs randomly extracted from the English-Italian version of MyMemory, the world’s largest public TM. Our results show competitive performance not only against a strong baseline that exploits machine translation, but also against a state-of-the-art method that relies on human-labelled data

transcrater a tool for automatic speech recognition quality estimation

We present TranscRater, an open-source tool for automatic speech recognition (ASR) quality estimation (QE). The tool allows users to perform ASR evaluation bypassing the need of reference transcripts and confidence information, which is common to current assessment protocols. TranscRater includes: i) methods to extract a variety of quality indicators from (signal, transcription) pairs and ii) machine learning algorithms which make possible to build ASR QE models exploiting the extracted features. Confirming the positive results of previous evaluations, new experiments with TranscRater indicate its effectiveness both in WER prediction and transcription ranking tasks

TMop: a Tool for Unsupervised Translation Memory Cleaning

We present TMop, the first open-source tool for automatic Translation Memory (TM) cleaning. The tool implements a fully unsupervised approach to the task, which allows spotting unreliable translation units (sentence pairs in different languages, which are supposed to be translations of each other) without requiring labeled training data. TMop includes a highly configurable and extensible set of filters capturing different aspects of translation quality. It has been evaluated on a test set composed of 1,000 translation units (TUs) randomly extracted from the English-Italian version of MyMemory, a large-scale public TM. Results indicate its effectiveness in automatic removing “bad” TUs, with comparable performance to a state-of-the-art supervised method (76.3 vs. 77.7 balanced accuracy)