Christophe Cerisara


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Multi-task dialog act and sentiment recognition on Mastodon
Christophe Cerisara | Somayeh Jafaritazehjani | Adedayo Oluokun | Hoa T. Le
Proceedings of the 27th International Conference on Computational Linguistics

Because of license restrictions, it often becomes impossible to strictly reproduce most research results on Twitter data already a few months after the creation of the corpus. This situation worsened gradually as time passes and tweets become inaccessible. This is a critical issue for reproducible and accountable research on social media. We partly solve this challenge by annotating a new Twitter-like corpus from an alternative large social medium with licenses that are compatible with reproducible experiments: Mastodon. We manually annotate both dialogues and sentiments on this corpus, and train a multi-task hierarchical recurrent network on joint sentiment and dialog act recognition. We experimentally demonstrate that transfer learning may be efficiently achieved between both tasks, and further analyze some specific correlations between sentiments and dialogues on social media. Both the annotated corpus and deep network are released with an open-source license.


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Weakly-supervised text-to-speech alignment confidence measure
Guillaume Serrière | Christophe Cerisara | Dominique Fohr | Odile Mella
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

This work proposes a new confidence measure for evaluating text-to-speech alignment systems outputs, which is a key component for many applications, such as semi-automatic corpus anonymization, lips syncing, film dubbing, corpus preparation for speech synthesis and speech recognition acoustic models training. This confidence measure exploits deep neural networks that are trained on large corpora without direct supervision. It is evaluated on an open-source spontaneous speech corpus and outperforms a confidence score derived from a state-of-the-art text-to-speech aligner. We further show that this confidence measure can be used to fine-tune the output of this aligner and improve the quality of the resulting alignment.


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A Domain Agnostic Approach to Verbalizing n-ary Events without Parallel Corpora
Bikash Gyawali | Claire Gardent | Christophe Cerisara
Proceedings of the 15th European Workshop on Natural Language Generation (ENLG)


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Unsupervised structured semantic inference for spoken dialog reservation tasks
Alejandra Lorenzo | Lina Rojas-Barahona | Christophe Cerisara
Proceedings of the SIGDIAL 2013 Conference


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Unsupervised frame based Semantic Role Induction: application to French and English
Alejandra Lorenzo | Christophe Cerisara
Proceedings of the ACL 2012 Joint Workshop on Statistical Parsing and Semantic Processing of Morphologically Rich Languages