Abstract
Mis-translation or dropping of proper nouns reduces the quality of machine translation output or speech recognition output as input of a dialog system. In this paper, we propose an automatic method of building a location dependent dictionary for speech recognition and speech translation systems. The method consists of two parts: location dependent word extraction and word classification. The first part extracts the word by using micro blog data based on Akaike’s information criteria. The second part classifies the words by using the Convolutional Neural Net (CNN) trained on crawled data. According to the experimental results, the method extracted around 2,000 location dependent words in the Tokyo area with 75% accuracy.
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Notes
- 1.
The actual hyper parameters’ setting is different from the example shown in the figure. Detail setting will be explained in Sect. 4.
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This research is supported by Japanese Ministry of Internal Affairs and Communications as a Global Communication Project.
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Yasuda, K., Heracleous, P., Ishikawa, A., Hashimoto, M., Matsumoto, K., Sugaya, F. (2018). Building a Location Dependent Dictionary for Speech Translation Systems. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2017. Lecture Notes in Computer Science(), vol 10762. Springer, Cham. https://doi.org/10.1007/978-3-319-77116-8_36
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