Search Results for author: Evgeny Matusov

Found 24 papers, 1 papers with code

Automatic Video Dubbing at AppTek

no code implementations EAMT 2022 Mattia Di Gangi, Nick Rossenbach, Alejandro Pérez, Parnia Bahar, Eugen Beck, Patrick Wilken, Evgeny Matusov

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.

AppTek’s Submission to the IWSLT 2022 Isometric Spoken Language Translation Task

no code implementations IWSLT (ACL) 2022 Patrick Wilken, Evgeny Matusov

To participate in the Isometric Spoken Language Translation Task of the IWSLT 2022 evaluation, constrained condition, AppTek developed neural Transformer-based systems for English-to-German with various mechanisms of length control, ranging from source-side and target-side pseudo-tokens to encoding of remaining length in characters that replaces positional encoding.

Translation

SubER - A Metric for Automatic Evaluation of Subtitle Quality

no code implementations IWSLT (ACL) 2022 Patrick Wilken, Panayota Georgakopoulou, Evgeny Matusov

This paper addresses the problem of evaluating the quality of automatically generated subtitles, which includes not only the quality of the machine-transcribed or translated speech, but also the quality of line segmentation and subtitle timing.

Translation

AppTek's Submission to the IWSLT 2022 Isometric Spoken Language Translation Task

no code implementations12 May 2022 Patrick Wilken, Evgeny Matusov

To participate in the Isometric Spoken Language Translation Task of the IWSLT 2022 evaluation, constrained condition, AppTek developed neural Transformer-based systems for English-to-German with various mechanisms of length control, ranging from source-side and target-side pseudo-tokens to encoding of remaining length in characters that replaces positional encoding.

Translation

SubER: A Metric for Automatic Evaluation of Subtitle Quality

no code implementations11 May 2022 Patrick Wilken, Panayota Georgakopoulou, Evgeny Matusov

This paper addresses the problem of evaluating the quality of automatically generated subtitles, which includes not only the quality of the machine-transcribed or translated speech, but also the quality of line segmentation and subtitle timing.

Translation

Neural Simultaneous Speech Translation Using Alignment-Based Chunking

no code implementations WS 2020 Patrick Wilken, Tamer Alkhouli, Evgeny Matusov, Pavel Golik

In simultaneous machine translation, the objective is to determine when to produce a partial translation given a continuous stream of source words, with a trade-off between latency and quality.

Chunking Machine Translation +3

Novel Applications of Factored Neural Machine Translation

no code implementations9 Oct 2019 Patrick Wilken, Evgeny Matusov

In this work, we explore the usefulness of target factors in neural machine translation (NMT) beyond their original purpose of predicting word lemmas and their inflections, as proposed by Garc\`ia-Mart\`inez et al., 2016.

Machine Translation NMT +1

Customizing Neural Machine Translation for Subtitling

no code implementations WS 2019 Evgeny Matusov, Patrick Wilken, Yota Georgakopoulou

In this work, we customized a neural machine translation system for translation of subtitles in the domain of entertainment.

Machine Translation Translation

Learning from Chunk-based Feedback in Neural Machine Translation

no code implementations ACL 2018 Pavel Petrushkov, Shahram Khadivi, Evgeny Matusov

We empirically investigate learning from partial feedback in neural machine translation (NMT), when partial feedback is collected by asking users to highlight a correct chunk of a translation.

Machine Translation NMT +1

Can Neural Machine Translation be Improved with User Feedback?

no code implementations NAACL 2018 Julia Kreutzer, Shahram Khadivi, Evgeny Matusov, Stefan Riezler

We present the first real-world application of methods for improving neural machine translation (NMT) with human reinforcement, based on explicit and implicit user feedback collected on the eBay e-commerce platform.

Machine Translation NMT +1

Neural and Statistical Methods for Leveraging Meta-information in Machine Translation

no code implementations MTSummit 2017 Shahram Khadivi, Patrick Wilken, Leonard Dahlmann, Evgeny Matusov

In this paper, we discuss different methods which use meta information and richer context that may accompany source language input to improve machine translation quality.

Machine Translation Translation

Human Evaluation of Multi-modal Neural Machine Translation: A Case-Study on E-Commerce Listing Titles

no code implementations WS 2017 Iacer Calixto, Daniel Stein, Evgeny Matusov, Sheila Castilho, Andy Way

Nonetheless, human evaluators ranked translations from a multi-modal NMT model as better than those of a text-only NMT over 88{\%} of the time, which suggests that images do help NMT in this use-case.

Machine Translation NMT +1

Using Images to Improve Machine-Translating E-Commerce Product Listings.

no code implementations EACL 2017 Iacer Calixto, Daniel Stein, Evgeny Matusov, Pintu Lohar, Sheila Castilho, Andy Way

We evaluate our models quantitatively using BLEU and TER and find that (i) additional synthetic data has a general positive impact on text-only and multi-modal NMT models, and that (ii) using a multi-modal NMT model for re-ranking n-best lists improves TER significantly across different n-best list sizes.

Machine Translation NMT +2

Guided Alignment Training for Topic-Aware Neural Machine Translation

2 code implementations AMTA 2016 Wenhu Chen, Evgeny Matusov, Shahram Khadivi, Jan-Thorsten Peter

In this paper, we propose an effective way for biasing the attention mechanism of a sequence-to-sequence neural machine translation (NMT) model towards the well-studied statistical word alignment models.

Domain Adaptation Machine Translation +3

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