40 research outputs found
The Uppsala-FBK systems at WMT 2011
This paper presents our submissions to the shared translation task at WMT 2011. We created two largely independent systems for English-to-French and Haitian Creole-to-English translation to evaluate different features and components from our ongoing research on these language pairs. Key features of our systems include anaphora resolution, hierarchical lexical reordering, data selection for language modelling, linear transduction grammars for word alignment and syntax-based decoding with monolingual dependency information
Combined Spoken Language Translation
EU-BRIDGE is a European research project which is aimed at developing innovative speech translation technology. One of the collaborative efforts within EU-BRIDGE is to produce joint submissions of up to four different partners to the evaluation campaign at the 2014 International Workshop on Spoken Language Translation (IWSLT). We submitted combined translations to the GermanâEnglish spoken language translation (SLT) track as well as to the GermanâEnglish, EnglishâGerman and EnglishâFrench machine translation (MT) tracks. In this paper, we present the techniques which were applied by the different individual translation systems of RWTH Aachen University, the University of Edinburgh, Karlsruhe Institute of Technology, and Fondazione Bruno Kessler. We then show the combination approach developed at RWTH Aachen University which combined the individual systems. The consensus translations yield empirical gains of up to 2.3 points in BLEU and 1.2 points in TER compared to the best individual system
An Open Source Toolkit for Word-level Confidence Estimation in Machine Translation
International audienceRecently, a growing need of Confidence Estimation (CE) for Statistical Machine Translation (SMT) systems in Computer Aided Translation (CAT), was observed. However, most of the CE toolkits are optimized for a single target language (mainly English) and, as far as we know, none of them are dedicated to this specific task and freely available. This paper presents an open-source toolkit for predicting the quality of words of a SMT output, whose novel contributions are (i) support for various target languages, (ii) handle a number of features of different types (system-based, lexical , syntactic and semantic). In addition, the toolkit also integrates a wide variety of Natural Language Processing or Machine Learning tools to pre-process data, extract features and estimate confidence at word-level. Features for Word-level Confidence Estimation (WCE) can be easily added / removed using a configuration file. We validate the toolkit by experimenting in the WCE evaluation framework of WMT shared task with two language pairs: French-English and English-Spanish. The toolkit is made available to the research community with ready-made scripts to launch full experiments on these language pairs, while achieving state-of-the-art and reproducible performances
Findings of the 2017 Conference on Machine Translation
This paper presents the results of the
WMT17 shared tasks, which included
three machine translation (MT) tasks
(news, biomedical, and multimodal), two
evaluation tasks (metrics and run-time estimation
of MT quality), an automatic
post-editing task, a neural MT training
task, and a bandit learning task
Findings of the 2011 Workshop on Statistical Machine Translation
This paper presents the results of the WMT11 shared tasks, which included a translation task, a system combination task, and a task for machine translation evaluation metrics. We conducted a large-scale manual evaluation of 148 machine translation systems and 41 system combination entries. We used the ranking of these systems to measure how strongly automatic metrics correlate with human judgments of translation quality for 21 evaluation metrics. This year featured a Haitian Creole to English task translating SMS messages sent to an emergency response service in the aftermath of the Haitian earthquake. We also conducted a pilot 'tunable metrics' task to test whether optimizing a fixed system to different metrics would result in perceptibly different translation quality
Automatic Quality Estimation for ASR System Combination
Recognizer Output Voting Error Reduction (ROVER) has been widely used for
system combination in automatic speech recognition (ASR). In order to select
the most appropriate words to insert at each position in the output
transcriptions, some ROVER extensions rely on critical information such as
confidence scores and other ASR decoder features. This information, which is
not always available, highly depends on the decoding process and sometimes
tends to over estimate the real quality of the recognized words. In this paper
we propose a novel variant of ROVER that takes advantage of ASR quality
estimation (QE) for ranking the transcriptions at "segment level" instead of:
i) relying on confidence scores, or ii) feeding ROVER with randomly ordered
hypotheses. We first introduce an effective set of features to compensate for
the absence of ASR decoder information. Then, we apply QE techniques to perform
accurate hypothesis ranking at segment-level before starting the fusion
process. The evaluation is carried out on two different tasks, in which we
respectively combine hypotheses coming from independent ASR systems and
multi-microphone recordings. In both tasks, it is assumed that the ASR decoder
information is not available. The proposed approach significantly outperforms
standard ROVER and it is competitive with two strong oracles that e xploit
prior knowledge about the real quality of the hypotheses to be combined.
Compared to standard ROVER, the abs olute WER improvements in the two
evaluation scenarios range from 0.5% to 7.3%
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