Masataka Goto


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Lyrics Information Processing: Analysis, Generation, and Applications
Kento Watanabe | Masataka Goto
Proceedings of the 1st Workshop on NLP for Music and Audio (NLP4MusA)


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A Melody-Conditioned Lyrics Language Model
Kento Watanabe | Yuichiroh Matsubayashi | Satoru Fukayama | Masataka Goto | Kentaro Inui | Tomoyasu Nakano
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

This paper presents a novel, data-driven language model that produces entire lyrics for a given input melody. Previously proposed models for lyrics generation suffer from the inability of capturing the relationship between lyrics and melody partly due to the unavailability of lyrics-melody aligned data. In this study, we first propose a new practical method for creating a large collection of lyrics-melody aligned data and then create a collection of 1,000 lyrics-melody pairs augmented with precise syllable-note alignments and word/sentence/paragraph boundaries. We then provide a quantitative analysis of the correlation between word/sentence/paragraph boundaries in lyrics and melodies. We then propose an RNN-based lyrics language model conditioned on a featurized melody. Experimental results show that the proposed model generates fluent lyrics while maintaining the compatibility between boundaries of lyrics and melody structures.


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Modeling Discourse Segments in Lyrics Using Repeated Patterns
Kento Watanabe | Yuichiroh Matsubayashi | Naho Orita | Naoaki Okazaki | Kentaro Inui | Satoru Fukayama | Tomoyasu Nakano | Jordan Smith | Masataka Goto
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

This study proposes a computational model of the discourse segments in lyrics to understand and to model the structure of lyrics. To test our hypothesis that discourse segmentations in lyrics strongly correlate with repeated patterns, we conduct the first large-scale corpus study on discourse segments in lyrics. Next, we propose the task to automatically identify segment boundaries in lyrics and train a logistic regression model for the task with the repeated pattern and textual features. The results of our empirical experiments illustrate the significance of capturing repeated patterns in predicting the boundaries of discourse segments in lyrics.


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Modeling Structural Topic Transitions for Automatic Lyrics Generation
Kento Watanabe | Yuichiroh Matsubayashi | Kentaro Inui | Masataka Goto
Proceedings of the 28th Pacific Asia Conference on Language, Information and Computing


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PodCastle: A Spoken Document Retrieval Service Improved by Anonymous User Contributions
Masataka Goto | Jun Ogata
Proceedings of the 24th Pacific Asia Conference on Language, Information and Computation