Arkady Arkhangorodsky


2021

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Findings of the 2021 Conference on Machine Translation (WMT21)
Farhad Akhbardeh | Arkady Arkhangorodsky | Magdalena Biesialska | Ondřej Bojar | Rajen Chatterjee | Vishrav Chaudhary | Marta R. Costa-jussa | Cristina España-Bonet | Angela Fan | Christian Federmann | Markus Freitag | Yvette Graham | Roman Grundkiewicz | Barry Haddow | Leonie Harter | Kenneth Heafield | Christopher Homan | Matthias Huck | Kwabena Amponsah-Kaakyire | Jungo Kasai | Daniel Khashabi | Kevin Knight | Tom Kocmi | Philipp Koehn | Nicholas Lourie | Christof Monz | Makoto Morishita | Masaaki Nagata | Ajay Nagesh | Toshiaki Nakazawa | Matteo Negri | Santanu Pal | Allahsera Auguste Tapo | Marco Turchi | Valentin Vydrin | Marcos Zampieri
Proceedings of the Sixth Conference on Machine Translation

This paper presents the results of the newstranslation task, the multilingual low-resourcetranslation for Indo-European languages, thetriangular translation task, and the automaticpost-editing task organised as part of the Con-ference on Machine Translation (WMT) 2021.In the news task, participants were asked tobuild machine translation systems for any of10 language pairs, to be evaluated on test setsconsisting mainly of news stories. The taskwas also opened up to additional test suites toprobe specific aspects of translation.

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MeetDot: Videoconferencing with Live Translation Captions
Arkady Arkhangorodsky | Christopher Chu | Scot Fang | Yiqi Huang | Denglin Jiang | Ajay Nagesh | Boliang Zhang | Kevin Knight
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

We present MeetDot, a videoconferencing system with live translation captions overlaid on screen. The system aims to facilitate conversation between people who speak different languages, thereby reducing communication barriers between multilingual participants. Currently, our system supports speech and captions in 4 languages and combines automatic speech recognition (ASR) and machine translation (MT) in a cascade. We use the re-translation strategy to translate the streamed speech, resulting in caption flicker. Additionally, our system has very strict latency requirements to have acceptable call quality. We implement several features to enhance user experience and reduce their cognitive load, such as smooth scrolling captions and reducing caption flicker. The modular architecture allows us to integrate different ASR and MT services in our backend. Our system provides an integrated evaluation suite to optimize key intrinsic evaluation metrics such as accuracy, latency and erasure. Finally, we present an innovative cross-lingual word-guessing game as an extrinsic evaluation metric to measure end-to-end system performance. We plan to make our system open-source for research purposes.

2020

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DiDi Labs’ End-to-end System for the IWSLT 2020 Offline Speech TranslationTask
Arkady Arkhangorodsky | Yiqi Huang | Amittai Axelrod
Proceedings of the 17th International Conference on Spoken Language Translation

This paper describes the system that was submitted by DiDi Labs to the offline speech translation task for IWSLT 2020. We trained an end-to-end system that translates audio from English TED talks to German text, without producing intermediate English text. We use the S-Transformer architecture and train using the MuSTC dataset. We also describe several additional experiments that were attempted, but did not yield improved results.