Using collocation segmentation to augment the phrase table

This paper describes the 2010 phrase-based statistical machine translation system developed at the TALP Research Center of the UPC1 in cooperation with BMIC2 and VMU3. In phrase-based SMT, the phrase table is the main tool in translation. It is created extracting phrases from an aligned parallel corpus and then computing translation model scores with them. Performing a collocation segmentation over the source and target corpus before the alignment causes that di erent and larger phrases are extracted from the same original documents. We performed this segmentation and used the union of this phrase set with the phrase set extracted from the nonsegmented corpus to compute the phrase table. We present the con gurations considered and also report results obtained with internal and o cial test sets.Postprint (published version

The impact of morphological errors in phrase-based statistical machine translation from German and English into Swedish

We have investigated the potential for improvement in target language morphology when translating into Swedish from English and German, by measuring the errors made by a state of the art phrase-based statistical machine translation system. Our results show that there is indeed a performance gap to be filled by better modelling of inflectional morphology and compounding; and that the gap is not filled by simply feeding the translation system with more training data

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 analysis of machine translation errors on the effectiveness of an Arabic-English QA system

The aim of this paper is to investigate how much the effectiveness of a Question Answering (QA) system was affected by the performance of Machine Translation (MT) based question translation. Nearly 200 questions were selected from TREC QA tracks and ran through a question answering system. It was able to answer 42.6% of the questions correctly in a monolingual run. These questions were then translated manually from English into Arabic and back into English using an MT system, and then re-applied to the QA system. The system was able to answer 10.2% of the translated questions. An analysis of what sort of translation error affected which questions was conducted, concluding that factoid type questions are less prone to translation error than others

Results of the WMT19 metrics shared task: segment-level and strong MT systems pose big challenges

This paper presents the results of the WMT19 Metrics Shared Task. Participants were asked to score the outputs of the translations systems competing in the WMT19 News Translation Task with automatic metrics. 13 research groups submitted 24 metrics, 10 of which are reference-less "metrics" and constitute submissions to the joint task with WMT19 Quality Estimation Task, "QE as a Metric". In addition, we computed 11 baseline metrics, with 8 commonly applied baselines (BLEU, SentBLEU, NIST, WER, PER, TER, CDER, and chrF) and 3 reimplementations (chrF+, sacreBLEU-BLEU, and sacreBLEU-chrF). Metrics were evaluated on the system level, how well a given metric correlates with the WMT19 official manual ranking, and segment level, how well the metric correlates with human judgements of segment quality. This year, we use direct assessment (DA) as our only form of manual evaluation