Souhir Gahbiche


2022

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Speech Resources in the Tamasheq Language
Marcely Zanon Boito | Fethi Bougares | Florentin Barbier | Souhir Gahbiche | Loïc Barrault | Mickael Rouvier | Yannick Estève
Proceedings of the Thirteenth Language Resources and Evaluation Conference

In this paper we present two datasets for Tamasheq, a developing language mainly spoken in Mali and Niger. These two datasets were made available for the IWSLT 2022 low-resource speech translation track, and they consist of collections of radio recordings from daily broadcast news in Niger (Studio Kalangou) and Mali (Studio Tamani). We share (i) a massive amount of unlabeled audio data (671 hours) in five languages: French from Niger, Fulfulde, Hausa, Tamasheq and Zarma, and (ii) a smaller 17 hours parallel corpus of audio recordings in Tamasheq, with utterance-level translations in the French language. All this data is shared under the Creative Commons BY-NC-ND 3.0 license. We hope these resources will inspire the speech community to develop and benchmark models using the Tamasheq language.

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Findings of the IWSLT 2022 Evaluation Campaign
Antonios Anastasopoulos | Loïc Barrault | Luisa Bentivogli | Marcely Zanon Boito | Ondřej Bojar | Roldano Cattoni | Anna Currey | Georgiana Dinu | Kevin Duh | Maha Elbayad | Clara Emmanuel | Yannick Estève | Marcello Federico | Christian Federmann | Souhir Gahbiche | Hongyu Gong | Roman Grundkiewicz | Barry Haddow | Benjamin Hsu | Dávid Javorský | Vĕra Kloudová | Surafel Lakew | Xutai Ma | Prashant Mathur | Paul McNamee | Kenton Murray | Maria Nǎdejde | Satoshi Nakamura | Matteo Negri | Jan Niehues | Xing Niu | John Ortega | Juan Pino | Elizabeth Salesky | Jiatong Shi | Matthias Sperber | Sebastian Stüker | Katsuhito Sudoh | Marco Turchi | Yogesh Virkar | Alexander Waibel | Changhan Wang | Shinji Watanabe
Proceedings of the 19th International Conference on Spoken Language Translation (IWSLT 2022)

The evaluation campaign of the 19th International Conference on Spoken Language Translation featured eight shared tasks: (i) Simultaneous speech translation, (ii) Offline speech translation, (iii) Speech to speech translation, (iv) Low-resource speech translation, (v) Multilingual speech translation, (vi) Dialect speech translation, (vii) Formality control for speech translation, (viii) Isometric speech translation. A total of 27 teams participated in at least one of the shared tasks. This paper details, for each shared task, the purpose of the task, the data that were released, the evaluation metrics that were applied, the submissions that were received and the results that were achieved.

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ON-TRAC Consortium Systems for the IWSLT 2022 Dialect and Low-resource Speech Translation Tasks
Marcely Zanon Boito | John Ortega | Hugo Riguidel | Antoine Laurent | Loïc Barrault | Fethi Bougares | Firas Chaabani | Ha Nguyen | Florentin Barbier | Souhir Gahbiche | Yannick Estève
Proceedings of the 19th International Conference on Spoken Language Translation (IWSLT 2022)

This paper describes the ON-TRAC Consortium translation systems developed for two challenge tracks featured in the Evaluation Campaign of IWSLT 2022: low-resource and dialect speech translation. For the Tunisian Arabic-English dataset (low-resource and dialect tracks), we build an end-to-end model as our joint primary submission, and compare it against cascaded models that leverage a large fine-tuned wav2vec 2.0 model for ASR. Our results show that in our settings pipeline approaches are still very competitive, and that with the use of transfer learning, they can outperform end-to-end models for speech translation (ST). For the Tamasheq-French dataset (low-resource track) our primary submission leverages intermediate representations from a wav2vec 2.0 model trained on 234 hours of Tamasheq audio, while our contrastive model uses a French phonetic transcription of the Tamasheq audio as input in a Conformer speech translation architecture jointly trained on automatic speech recognition, ST and machine translation losses. Our results highlight that self-supervised models trained on smaller sets of target data are more effective to low-resource end-to-end ST fine-tuning, compared to large off-the-shelf models. Results also illustrate that even approximate phonetic transcriptions can improve ST scores.

2020

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Arabizi Language Models for Sentiment Analysis
Gaétan Baert | Souhir Gahbiche | Guillaume Gadek | Alexandre Pauchet
Proceedings of the 28th International Conference on Computational Linguistics

Arabizi is a written form of spoken Arabic, relying on Latin characters and digits. It is informal and does not follow any conventional rules, raising many NLP challenges. In particular, Arabizi has recently emerged as the Arabic language in online social networks, becoming of great interest for opinion mining and sentiment analysis. Unfortunately, only few Arabizi resources exist and state-of-the-art language models such as BERT do not consider Arabizi. In this work, we construct and release two datasets: (i) LAD, a corpus of 7.7M tweets written in Arabizi and (ii) SALAD, a subset of LAD, manually annotated for sentiment analysis. Then, a BERT architecture is pre-trained on LAD, in order to create and distribute an Arabizi language model called BAERT. We show that a language model (BAERT) pre-trained on a large corpus (LAD) in the same language (Arabizi) as that of the fine-tuning dataset (SALAD), outperforms a state-of-the-art multi-lingual pretrained model (multilingual BERT) on a sentiment analysis task.