Introduction to the Special Section on Computational Modeling and Understanding of Emotions in Conflictual Social Interactions

The editorial work of C. Clavel for this special issue was partially supported by a grant overseen by the French National Research Agency (ANR17-MAOI) and by the European project H2020 ANIMATAS (MSCA-ITN-ETN 7659552). The editorial work of V. Patti was partially funded by Progetto di Ateneo/CSP 2016 (Immigrants, Hate and Prejudice in Social Media, S1618_L2_BOSC_01). P. Rosso was partially funded by Spanish MICINN under the research project MISMIS-FAKEnHATE on MISinformation and MIScommunication in social media: FAKE news and HATE speech (PGC2018-096212-B-C31).Damiano, R.; Patti, V.; Clavel, C.; Rosso, P. (2020). Introduction to the Special Section on Computational Modeling and Understanding of Emotions in Conflictual Social Interactions. ACM Transactions on Internet Technology. 20(2):1-5. https://doi.org/10.1145/3392334S15202Basile, V., Bosco, C., Fersini, E., Nozza, D., Patti, V., Rangel Pardo, F. M., … Sanguinetti, M. (2019). SemEval-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter. Proceedings of the 13th International Workshop on Semantic Evaluation. doi:10.18653/v1/s19-2007Bassignana, E., Basile, V., & Patti, V. (2018). Hurtlex: A Multilingual Lexicon of Words to Hurt. Proceedings of the Fifth Italian Conference on Computational Linguistics CLiC-it 2018, 51-56. doi:10.4000/books.aaccademia.3085Cristina Bosco Felice Dell’Orletta Fabio Poletto Manuela Sanguinetti and Maurizio Tesconi. 2018. Overview of the EVALITA 2018 hate speech detection task. In Proceedings of the 6th Evaluation Campaign of Natural Language Processing and Speech Tools for Italian. Final Workshop (EVALITA’18) co-located with the 5th Italian Conference on Computational Linguistics (CLiC-it’18). 9. http://ceur-ws.org/Vol-2263/paper010.pdf Cristina Bosco Felice Dell’Orletta Fabio Poletto Manuela Sanguinetti and Maurizio Tesconi. 2018. Overview of the EVALITA 2018 hate speech detection task. In Proceedings of the 6th Evaluation Campaign of Natural Language Processing and Speech Tools for Italian. Final Workshop (EVALITA’18) co-located with the 5th Italian Conference on Computational Linguistics (CLiC-it’18). 9. http://ceur-ws.org/Vol-2263/paper010.pdfBrady, W. J., Wills, J. A., Jost, J. T., Tucker, J. A., & Van Bavel, J. J. (2017). Emotion shapes the diffusion of moralized content in social networks. Proceedings of the National Academy of Sciences, 114(28), 7313-7318. doi:10.1073/pnas.1618923114Fortuna, P., & Nunes, S. (2018). A Survey on Automatic Detection of Hate Speech in Text. ACM Computing Surveys, 51(4), 1-30. doi:10.1145/3232676Pamungkas, E. W., & Patti, V. (2019). Cross-domain and Cross-lingual Abusive Language Detection: A Hybrid Approach with Deep Learning and a Multilingual Lexicon. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop. doi:10.18653/v1/p19-2051Plutchik, R. (2001). The Nature of Emotions. American Scientist, 89(4), 344. doi:10.1511/2001.4.344Schmidt, A., & Wiegand, M. (2017). A Survey on Hate Speech Detection using Natural Language Processing. Proceedings of the Fifth International Workshop on Natural Language Processing for Social Media. doi:10.18653/v1/w17-1101W. Wilmot and J. Hocker. 2013. Interpersonal Conflict (9th ed.). McGraw-Hill New York NY. W. Wilmot and J. Hocker. 2013. Interpersonal Conflict (9th ed.). McGraw-Hill New York NY

Advances in All-Neural Speech Recognition

This paper advances the design of CTC-based all-neural (or end-to-end) speech recognizers. We propose a novel symbol inventory, and a novel iterated-CTC method in which a second system is used to transform a noisy initial output into a cleaner version. We present a number of stabilization and initialization methods we have found useful in training these networks. We evaluate our system on the commonly used NIST 2000 conversational telephony test set, and significantly exceed the previously published performance of similar systems, both with and without the use of an external language model and decoding technology

Phonetic Temporal Neural Model for Language Identification

Deep neural models, particularly the LSTM-RNN model, have shown great potential for language identification (LID). However, the use of phonetic information has been largely overlooked by most existing neural LID methods, although this information has been used very successfully in conventional phonetic LID systems. We present a phonetic temporal neural model for LID, which is an LSTM-RNN LID system that accepts phonetic features produced by a phone-discriminative DNN as the input, rather than raw acoustic features. This new model is similar to traditional phonetic LID methods, but the phonetic knowledge here is much richer: it is at the frame level and involves compacted information of all phones. Our experiments conducted on the Babel database and the AP16-OLR database demonstrate that the temporal phonetic neural approach is very effective, and significantly outperforms existing acoustic neural models. It also outperforms the conventional i-vector approach on short utterances and in noisy conditions.Comment: Submitted to TASL

Transfer Learning for Speech and Language Processing

Transfer learning is a vital technique that generalizes models trained for one setting or task to other settings or tasks. For example in speech recognition, an acoustic model trained for one language can be used to recognize speech in another language, with little or no re-training data. Transfer learning is closely related to multi-task learning (cross-lingual vs. multilingual), and is traditionally studied in the name of `model adaptation'. Recent advance in deep learning shows that transfer learning becomes much easier and more effective with high-level abstract features learned by deep models, and the `transfer' can be conducted not only between data distributions and data types, but also between model structures (e.g., shallow nets and deep nets) or even model types (e.g., Bayesian models and neural models). This review paper summarizes some recent prominent research towards this direction, particularly for speech and language processing. We also report some results from our group and highlight the potential of this very interesting research field.Comment: 13 pages, APSIPA 201