Abstract
In text and speech, there are various features that express the individuality of the writer or speaker. In this paper, we take a step towards the creation of dialogue systems that consider this individuality by proposing a method for transforming individuality using a technique inspired by statistical machine translation (SMT). However, finding a parallel corpus with identical semantic content but different individuality is difficult, precluding the use of standard SMT techniques. Thus, in this paper, we focus on methods for creating a translation model (TM) using techniques from the paraphrasing literature and a language model (LM) by combining small amounts of individuality-rich data with larger amounts of background text. We perform an automatic and manual evaluation comparing the effectiveness of three types of TM construction techniques and find that the proposed system using a method focusing on a limited set of function words is most effective and can transform individuality to a degree that is both noticeable and identifiable.
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Notes
- 1.
This Japanese paraphrase model will be made available upon acceptance of the paper.
- 2.
- 3.
Verbosity is one component of individuality, so setting λ wp to a different value for each source/target speaker pair is more appropriate, but we leave this to future work.
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Mizukami, M., Neubig, G., Sakti, S., Toda, T., Nakamura, S. (2015). Linguistic Individuality Transformation for Spoken Language. In: Lee, G., Kim, H., Jeong, M., Kim, JH. (eds) Natural Language Dialog Systems and Intelligent Assistants. Springer, Cham. https://doi.org/10.1007/978-3-319-19291-8_13
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DOI: https://doi.org/10.1007/978-3-319-19291-8_13
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