Kento Watanabe


2020

pdf bib
Proceedings of the 1st Workshop on NLP for Music and Audio (NLP4MusA)
Sergio Oramas | Luis Espinosa-Anke | Elena Epure | Rosie Jones | Mohamed Sordo | Massimo Quadrana | Kento Watanabe
Proceedings of the 1st Workshop on NLP for Music and Audio (NLP4MusA)

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

2018

pdf bib
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.

pdf bib
Unsupervised Learning of Style-sensitive Word Vectors
Reina Akama | Kento Watanabe | Sho Yokoi | Sosuke Kobayashi | Kentaro Inui
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

This paper presents the first study aimed at capturing stylistic similarity between words in an unsupervised manner. We propose extending the continuous bag of words (CBOW) embedding model (Mikolov et al., 2013b) to learn style-sensitive word vectors using a wider context window under the assumption that the style of all the words in an utterance is consistent. In addition, we introduce a novel task to predict lexical stylistic similarity and to create a benchmark dataset for this task. Our experiment with this dataset supports our assumption and demonstrates that the proposed extensions contribute to the acquisition of style-sensitive word embeddings.

2016

pdf bib
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.

2014

pdf bib
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

2013

pdf bib
Extracting and Aggregating False Information from Microblogs
Naoaki Okazaki | Keita Nabeshima | Kento Watanabe | Junta Mizuno | Kentaro Inui
Proceedings of the Workshop on Language Processing and Crisis Information 2013