Articles

AbstractNeural machine translation (NMT) systems have demonstrated promising results in recent years. However, non-trivial amounts of manual effort are required for tuning network architectures, training configurations, and pre-processing settings such as byte pair encoding (BPE). In this study, we propose an evolution strategy based automatic tuning method for NMT. In …...

AbstractRecent work on multilingual neural machine translation reported competitive performance with respect to bilingual models and surprisingly good performance even on (zero-shot) translation directions not observed at training time. We investigate here a zero-shot translation in a particularly low-resource multilingual setting. We propose a simple iterative training procedure that leverages …...

AbstractIn this paper, we proposed two strategies which can be applied to a multilingual neural machine translation system in order to better tackle zero-shot scenarios despite not having any parallel corpus. The experiments show that they are effective in terms of both performance and computing resources, especially in multilingual translation …...

AbstractNeural machine translation (NMT) is the state of the art for machine translation, and it shows the best performance when there is a considerable amount of data available. When only little data exist for a language pair, the model cannot produce good representations for words, particularly for rare words. One …...

AbstractTranslating noisy inputs, such as the output of a speech recognizer, is a difficult but important challenge for neural machine translation. One way to increase robustness of neural models is by introducing artificial noise to the training data. In this paper, we experiment with appropriate forms of such noise, exploring …...

AbstractIn this paper, we introduce a novel approach to generate synthetic data for training Neural Machine Translation systems. The proposed approach supports language variants and dialects with very limited parallel training data. This is achieved using a seed data to project words from a closely-related resource-rich language to an under-resourced …...

AbstractPunctuation and segmentation is crucial in spoken language translation, as it has a strong impact to translation performance. However, the impact of rare or unknown words in the performance of punctuation and segmentation insertion has not been thoroughly studied. In this work, we simulate various degrees of domain-match in testing …...

AbstractIn this paper, we explore alternative ways to train a neural machine translation system in a multi-domain scenario. We investigate data concatenation (with fine tuning), model stacking (multi-level fine tuning), data selection and multi-model ensemble. Our findings show that the best translation quality can be achieved by building an initial …...

AbstractThis paper describes our German and English Speech-to-Text (STT) systems for the 2017 IWSLT evaluation campaign. The campaign focuses on the transcription of unsegmented lecture talks. Our setup includes systems using both the Janus and Kaldi frameworks. We combined the outputs using both ROVER [1] and confusion network combination (CNC) …...

AbstractWe describe here our Machine Translation (MT) model and the results we obtained for the IWSLT 2017 Multilingual Shared Task. Motivated by Zero Shot NMT [1] we trained a Multilingual Neural Machine Translation by combining all the training data into one single collection by appending the tokens to the source …...

AbstractIn this paper, we describe GTCOM’s neural machine translation(NMT) systems for the International Workshop on Spoken Language Translation(IWSLT) 2017. We participated in the English-to-Chinese and Chinese-to-English tracks in the small data condition of the bilingual task and the zero-shot condition of the multilingual task. Our systems are based on the …...

AbstractIn this paper, we present KIT’s multilingual neural machine translation (NMT) systems for the IWSLT 2017 evaluation campaign machine translation (MT) and spoken language translation (SLT) tasks. For our MT task submissions, we used our multi-task system, modified from a standard attentional neural machine translation framework, instead of building 20 …...

AbstractNeural Machine Translation has been shown to enable inference and cross-lingual knowledge transfer across multiple language directions using a single multilingual model. Focusing on this multilingual translation scenario, this work summarizes FBK’s participation in the IWSLT 2017 shared task. Our submissions rely on two multilingual systems trained on five languages …...

AbstractThis work describes the Neural Machine Translation (NMT) system of the RWTH Aachen University developed for the English$German tracks of the evaluation campaign of the International Workshop on Spoken Language Translation (IWSLT) 2017. We use NMT systems which are augmented by state-of-the-art extensions. Furthermore, we experiment with techniques that include …...

AbstractThis paper describes the joint submission of Samsung Research and Development, Warsaw, Poland and the University of Edinburgh team to the IWSLT MT task for TED talks. We took part in two translation directions, en-de and de-en. We also participated in the en-de and de-en lectures SLT task. The models …...

AbstractThe IWSLT 2017 evaluation campaign has organised three tasks. The Multilingual task, which is about training machine translation systems handling many-to-many language directions, including so-called zero-shot directions. The Dialogue task, which calls for the integration of context information in machine translation, in order to resolve anaphoric references that typically occur …...

AbstractThis paper describes the UdS-DFKI participation to the multilingual task of the IWSLT Evaluation 2017. Our approach is based on factored multilingual neural translation systems following the small data and zero-shot training conditions. Our systems are designed to fully exploit multilinguality by including factors that increase the number of common …...

AbstractThis paper proposes a novel method to select sentences for automatic summarization based on an evolutionary algorithm. The algorithm explores candidate summaries space following an objective function computed over ngrams probability distributions of the candidate summary and the source documents. This method does not consider a summary as a stack …...

AbstractDecision trees have been previously employed in many machine-learning tasks such as part-of-speech tagging, lemmatization, morphological-attribute resolution, letter-to-sound conversion and statistical-parametric speech synthesis. In this paper we introduce an optimized tree-computation algorithm, which is based on the original ID3 algorithm. We also introduce a tree-pruning method that uses a development …...

AbstractManual text annotation is an essential part of Big Text analytics. Although annotators work with limited parts of data sets, their results are extrapolated by automated text classification and affect the final classification results. Reliability of annotations and adequacy of assigned labels are especially important in the case of sentiment …...