Fine-tuning on Clean Data for End-to-End Speech Translation: FBK @ IWSLT 2018

This paper describes FBK's submission to the end-to-end English-German speech translation task at IWSLT 2018. Our system relies on a state-of-the-art model based on LSTMs and CNNs, where the CNNs are used to reduce the temporal dimension of the audio input, which is in general much higher than machine translation input. Our model was trained only on the audio-to-text parallel data released for the task, and fine-tuned on cleaned subsets of the original training corpus. The addition of weight normalization and label smoothing improved the baseline system by 1.0 BLEU point on our validation set. The final submission also featured checkpoint averaging within a training run and ensemble decoding of models trained during multiple runs. On test data, our best single model obtained a BLEU score of 9.7, while the ensemble obtained a BLEU score of 10.24.Comment: 6 pages, 2 figures, system description at the 15th International Workshop on Spoken Language Translation (IWSLT) 201

The ITC-irst statistical machine translation system for IWSLT-2004

Focus of this paper is the system for statistical machine translation developed at ITC-irst. It has been employed in the evaluation campaign of the International Workshop on Spoken Language Translation 2004 in all the three data set conditions of the Chinese-English track. Both the statistical model underlying the system and the system architecture are presented. Moreover, details are given on how the submitted runs have been produced. 1

Enhancing Transformer for End-to-end Speech-to-Text Translation

Neural end-to-end architectures have beenrecently proposed for spoken languagetranslation (SLT), following the state-of-the-art results obtained in machine translation (MT) and speech recognition (ASR).Motivated by this contiguity, we proposean SLT adaptation of Transformer (thestate-of-the-art architecture in MT), whichexploits the integration of ASR solutionsto cope with long input sequences featuring low information density. Long audiorepresentations hinder the training of largemodels due to Transformer’s quadraticmemory complexity.Moreover, for thesake of translation quality, handling suchsequences requires capturing both short-and long-range dependencies between bi-dimensional features. Focusing on Trans-former’s encoder, our adaptation is basedon:i)downsampling the input with con-volutional neural networks, which enablesmodel training on non cutting-edge GPUs,ii)modeling the bidimensional nature ofthe audio spectrogram with 2D components, andiii)adding a distance penaltyto the attention, which is able to bias ittowards short-range dependencies.Ourexperiments show that our SLT-adaptedTransformer outperforms the RNN-basedbaseline both in translation quality andtraining time, setting the state-of-the-artperformance on six language directions

Gender in Danger? Evaluating Speech Translation Technology on the MuST-SHE Corpus

Translating from languages without productive grammatical gender like English into gender-marked languages is a well-known difficulty for machines. This difficulty is also due to the fact that the training data on which models are built typically reflect the asymmetries of natural languages, gender bias included. Exclusively fed with textual data, machine translation is intrinsically constrained by the fact that the input sentence does not always contain clues about the gender identity of the referred human entities. But what happens with speech translation, where the input is an audio signal? Can audio provide additional information to reduce gender bias? We present the first thorough investigation of gender bias in speech translation, contributing with: i) the release of a benchmark useful for future studies, and ii) the comparison of different technologies (cascade and end-to-end) on two language directions (English-Italian/French).Comment: 9 pages of content, accepted at ACL 202

The Multilingual TEDx Corpus for Speech Recognition and Translation

We present the Multilingual TEDx corpus, built to support speech recognition (ASR) and speech translation (ST) research across many non-English source languages. The corpus is a collection of audio recordings from TEDx talks in 8 source languages. We segment transcripts into sentences and align them to the source-language audio and target-language translations. The corpus is released along with open-sourced code enabling extension to new talks and languages as they become available. Our corpus creation methodology can be applied to more languages than previous work, and creates multi-way parallel evaluation sets. We provide baselines in multiple ASR and ST settings, including multilingual models to improve translation performance for low-resource language pairs

The IWSLT 2016 Evaluation Campaign

The IWSLT 2016 Evaluation Campaign featured two tasks: the translation of talks and the translation of video conference conversations. While the first task extends previously offered tasks with talks from a different source, the second task is completely new. For both tasks, three tracks were organised: automatic speech recognition (ASR), spoken language translation (SLT), and machine translation (MT). Main translation directions that were offered are English to/from German and English to French. Additionally, the MT track included English to/from Arabic and Czech, as well as French to English. We received this year run submissions from 11 research labs. All runs were evaluated with objective metrics, while submissions for two of the MT talk tasks were also evaluated with human post-editing. Results of the human evaluation show improvements over the best submissions of last year

The IWSLT 2018 Evaluation Campaign

The InternationalWorkshop of Spoken Language Translation (IWSLT) 2018 Evaluation Campaign featured two tasks: the low-resourced machine translation task and the speech translation task. In the first task, manual transcribed speech needs to be translated from Basque to English. Since this translation direction is a under-resourced language pair, participants were encouraged to used additional parallel data from related languages. In the second task, the participants need to translate English audio into German text by building a full speech-translation system. In the baseline condition, participants were free to used any architecture, while they are restricted to use a single model for the end-to-end task. This year, eight research groups took part in the Basque English translation task, and nine in the speech translation tas