Abstract
In a conversation, humans use changes in a dialogue to predict safety-critical situations and use them to react accordingly. We propose to use the same cues for safer human-robot interaction for early verbal detection of dangerous situations. Due to the limited availability of sentiment-annotated dialogue corpora, we use a simple sentiment classification for utterances to neurally learn sentiment changes within dialogues and ultimately predict the sentiment of upcoming utterances. We train a recurrent neural network on context sequences of words, defined as two utterances of each speaker, to predict the sentiment class of the next utterance. Our results show that this leads to useful predictions of the sentiment class of the upcoming utterance. Results for two challenging dialogue datasets are reported to show that predictions are similar independent of the dataset used for training. The prediction accuracy is about 63% for binary and 58% for multi-class classification.
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Acknowledgement
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 642667 (SECURE).
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Bothe, C., Magg, S., Weber, C., Wermter, S. (2017). Dialogue-Based Neural Learning to Estimate the Sentiment of a Next Upcoming Utterance. In: Lintas, A., Rovetta, S., Verschure, P., Villa, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2017. ICANN 2017. Lecture Notes in Computer Science(), vol 10614. Springer, Cham. https://doi.org/10.1007/978-3-319-68612-7_54
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