Parnia Bahar


2022

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Automatic Video Dubbing at AppTek
Mattia Di Gangi | Nick Rossenbach | Alejandro Pérez | Parnia Bahar | Eugen Beck | Patrick Wilken | Evgeny Matusov
Proceedings of the 23rd Annual Conference of the European Association for Machine Translation

Video dubbing is the activity of revoicing a video while offering a viewing experience equivalent to the original video. The revoicing usually comes with a changed script, mostly in a different language, and the revoicing should reproduce the original emotions, coherent with the body language, and lip synchronized. In this project, we aim to build an AD system in three phases: (1) voice-over; (2) emotional voice-over; (3) full dubbing, while enhancing the system with human-in-the-loop capabilities for a higher quality.

2021

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Without Further Ado: Direct and Simultaneous Speech Translation by AppTek in 2021
Parnia Bahar | Patrick Wilken | Mattia A. Di Gangi | Evgeny Matusov
Proceedings of the 18th International Conference on Spoken Language Translation (IWSLT 2021)

This paper describes the offline and simultaneous speech translation systems developed at AppTek for IWSLT 2021. Our offline ST submission includes the direct end-to-end system and the so-called posterior tight integrated model, which is akin to the cascade system but is trained in an end-to-end fashion, where all the cascaded modules are end-to-end models themselves. For simultaneous ST, we combine hybrid automatic speech recognition with a machine translation approach whose translation policy decisions are learned from statistical word alignments. Compared to last year, we improve general quality and provide a wider range of quality/latency trade-offs, both due to a data augmentation method making the MT model robust to varying chunk sizes. Finally, we present a method for ASR output segmentation into sentences that introduces a minimal additional delay.

2020

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Start-Before-End and End-to-End: Neural Speech Translation by AppTek and RWTH Aachen University
Parnia Bahar | Patrick Wilken | Tamer Alkhouli | Andreas Guta | Pavel Golik | Evgeny Matusov | Christian Herold
Proceedings of the 17th International Conference on Spoken Language Translation

AppTek and RWTH Aachen University team together to participate in the offline and simultaneous speech translation tracks of IWSLT 2020. For the offline task, we create both cascaded and end-to-end speech translation systems, paying attention to careful data selection and weighting. In the cascaded approach, we combine high-quality hybrid automatic speech recognition (ASR) with the Transformer-based neural machine translation (NMT). Our end-to-end direct speech translation systems benefit from pretraining of adapted encoder and decoder components, as well as synthetic data and fine-tuning and thus are able to compete with cascaded systems in terms of MT quality. For simultaneous translation, we utilize a novel architecture that makes dynamic decisions, learned from parallel data, to determine when to continue feeding on input or generate output words. Experiments with speech and text input show that even at low latency this architecture leads to superior translation results.

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Investigation of Transformer-based Latent Attention Models for Neural Machine Translation
Parnia Bahar | Nikita Makarov | Hermann Ney
Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)

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Successfully Applying the Stabilized Lottery Ticket Hypothesis to the Transformer Architecture
Christopher Brix | Parnia Bahar | Hermann Ney
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Sparse models require less memory for storage and enable a faster inference by reducing the necessary number of FLOPs. This is relevant both for time-critical and on-device computations using neural networks. The stabilized lottery ticket hypothesis states that networks can be pruned after none or few training iterations, using a mask computed based on the unpruned converged model. On the transformer architecture and the WMT 2014 English-to-German and English-to-French tasks, we show that stabilized lottery ticket pruning performs similar to magnitude pruning for sparsity levels of up to 85%, and propose a new combination of pruning techniques that outperforms all other techniques for even higher levels of sparsity. Furthermore, we confirm that the parameter’s initial sign and not its specific value is the primary factor for successful training, and show that magnitude pruning cannot be used to find winning lottery tickets.

2019

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On Using SpecAugment for End-to-End Speech Translation
Parnia Bahar | Albert Zeyer | Ralf Schlüter | Hermann Ney
Proceedings of the 16th International Conference on Spoken Language Translation

This work investigates a simple data augmentation technique, SpecAugment, for end-to-end speech translation. SpecAugment is a low-cost implementation method applied directly to the audio input features and it consists of masking blocks of frequency channels, and/or time steps. We apply SpecAugment on end-to-end speech translation tasks and achieve up to +2.2% BLEU on LibriSpeech Audiobooks En→Fr and +1.2% on IWSLT TED-talks En→De by alleviating overfitting to some extent. We also examine the effectiveness of the method in a variety of data scenarios and show that the method also leads to significant improvements in various data conditions irrespective of the amount of training data.

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The RWTH Aachen University Machine Translation Systems for WMT 2019
Jan Rosendahl | Christian Herold | Yunsu Kim | Miguel Graça | Weiyue Wang | Parnia Bahar | Yingbo Gao | Hermann Ney
Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)

This paper describes the neural machine translation systems developed at the RWTH Aachen University for the German-English, Chinese-English and Kazakh-English news translation tasks of the Fourth Conference on Machine Translation (WMT19). For all tasks, the final submitted system is based on the Transformer architecture. We focus on improving data filtering and fine-tuning as well as systematically evaluating interesting approaches like unigram language model segmentation and transfer learning. For the De-En task, none of the tested methods gave a significant improvement over last years winning system and we end up with the same performance, resulting in 39.6% BLEU on newstest2019. In the Zh-En task, we show 1.3% BLEU improvement over our last year’s submission, which we mostly attribute to the splitting of long sentences during translation. We further report results on the Kazakh-English task where we gain improvements of 11.1% BLEU over our baseline system. On the same task we present a recent transfer learning approach, which uses half of the free parameters of our submission system and performs on par with it.

2018

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Towards Two-Dimensional Sequence to Sequence Model in Neural Machine Translation
Parnia Bahar | Christopher Brix | Hermann Ney
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

This work investigates an alternative model for neural machine translation (NMT) and proposes a novel architecture, where we employ a multi-dimensional long short-term memory (MDLSTM) for translation modelling. In the state-of-the-art methods, source and target sentences are treated as one-dimensional sequences over time, while we view translation as a two-dimensional (2D) mapping using an MDLSTM layer to define the correspondence between source and target words. We extend beyond the current sequence to sequence backbone NMT models to a 2D structure in which the source and target sentences are aligned with each other in a 2D grid. Our proposed topology shows consistent improvements over attention-based sequence to sequence model on two WMT 2017 tasks, German<->English.

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Neural Speech Translation at AppTek
Evgeny Matusov | Patrick Wilken | Parnia Bahar | Julian Schamper | Pavel Golik | Albert Zeyer | Joan Albert Silvestre-Cerda | Adrià Martínez-Villaronga | Hendrik Pesch | Jan-Thorsten Peter
Proceedings of the 15th International Conference on Spoken Language Translation

This work describes AppTek’s speech translation pipeline that includes strong state-of-the-art automatic speech recognition (ASR) and neural machine translation (NMT) components. We show how these components can be tightly coupled by encoding ASR confusion networks, as well as ASR-like noise adaptation, vocabulary normalization, and implicit punctuation prediction during translation. In another experimental setup, we propose a direct speech translation approach that can be scaled to translation tasks with large amounts of text-only parallel training data but a limited number of hours of recorded and human-translated speech.

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The RWTH Aachen University Supervised Machine Translation Systems for WMT 2018
Julian Schamper | Jan Rosendahl | Parnia Bahar | Yunsu Kim | Arne Nix | Hermann Ney
Proceedings of the Third Conference on Machine Translation: Shared Task Papers

This paper describes the statistical machine translation systems developed at RWTH Aachen University for the German→English, English→Turkish and Chinese→English translation tasks of the EMNLP 2018 Third Conference on Machine Translation (WMT 2018). We use ensembles of neural machine translation systems based on the Transformer architecture. Our main focus is on the German→English task where we to all automatic scored first with respect metrics provided by the organizers. We identify data selection, fine-tuning, batch size and model dimension as important hyperparameters. In total we improve by 6.8% BLEU over our last year’s submission and by 4.8% BLEU over the winning system of the 2017 German→English task. In English→Turkish task, we show 3.6% BLEU improvement over the last year’s winning system. We further report results on the Chinese→English task where we improve 2.2% BLEU on average over our baseline systems but stay behind the 2018 winning systems.

2017

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The RWTH Aachen Machine Translation Systems for IWSLT 2017
Parnia Bahar | Jan Rosendahl | Nick Rossenbach | Hermann Ney
Proceedings of the 14th International Conference on Spoken Language Translation

This 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 data filtering, a larger vocabulary, two extensions to the attention mechanism and domain adaptation. Using these methods, we can show considerable improvements over the respective baseline systems and our IWSLT 2016 submission.

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The RWTH Aachen University English-German and German-English Machine Translation System for WMT 2017
Jan-Thorsten Peter | Andreas Guta | Tamer Alkhouli | Parnia Bahar | Jan Rosendahl | Nick Rossenbach | Miguel Graça | Hermann Ney
Proceedings of the Second Conference on Machine Translation

2015

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The RWTH Aachen machine translation system for IWSLT 2015
Jan-Thorsten Peter | Farzad Toutounchi | Stephan Peitz | Parnia Bahar | Andreas Guta | Hermann Ney
Proceedings of the 12th International Workshop on Spoken Language Translation: Evaluation Campaign