Papers by Ryo Masumura
Generating Responses that Reflect Meta Information in User-Generated Question Answer Pairs (2020.lrec-1)
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Takashi Kodama, Ryuichiro Higashinaka, Koh Mitsuda, Ryo Masumura, Yushi Aono, Ryuta Nakamura, Noritake Adachi, Hidetoshi Kawabata
| Challenge: | Existing approaches to realize consistent personalities require expensive data collection. |
| Approach: | They propose to collect question-answer pairs for particular characters from online users . meta information such as emotion and intimacy was also collected . |
| Outcome: | The proposed method can be used to train neural conversational models with high quality questions and meta information. |
Multimodal Negotiation Corpus with Various Subjective Assessments for Social-Psychological Outcome Prediction from Non-Verbal Cues (2022.lrec-1)
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| Challenge: | Existing corpora only include information related to objective outcomes or a single aspect of psychology. |
| Approach: | They investigated social-psychological negotiation-outcome prediction task from negotiation dialogue data. |
| Outcome: | The proposed task is useful because negotiation data only include objective outcomes or a single aspect of psychology. |
Parallel Corpus for Japanese Spoken-to-Written Style Conversion (2020.lrec-1)
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| Challenge: | spoken-to-written style conversion is becoming an important technology to increase the readability of ASR transcriptions. |
| Approach: | They propose to build a Japanese parallel corpus of spoken-to-written style conversions . they use crowdsourcing to convert spoken-style text into written-style texts . |
| Outcome: | The proposed corpus can handle general and specific spoken-to-written style conversion problems in Japanese. |
Multi-Perspective Document Revision (2022.coling-1)
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| Challenge: | a novel document revision task that revises multiple perspectives is proposed . grammatical error correction tasks have been studied in the natural language processing field . |
| Approach: | They propose a Japanese multi-perspective document revision task that revises multiple perspectives to improve the readability and clarity of a document. |
| Outcome: | The proposed model can be used to improve the readability and clarity of a document. |
Multi-task and Multi-lingual Joint Learning of Neural Lexical Utterance Classification based on Partially-shared Modeling (C18-1)
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| Challenge: | Existing studies on multitask and multilingual joint learning focus on cross-task or cross-lingual knowledge transfer. |
| Approach: | They propose to divide state-of-the-art neural lexical utterance classification into language-specific components that can be shared between different tasks and different languages. |
| Outcome: | The proposed method is able to support multi-task and multi-lingual learning using Japanese and English data sets with three different lexical utterance classification tasks. |
DNN-based Speech Synthesis Using Abundant Tags of Spontaneous Speech Corpus (2020.lrec-1)
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Yuki Yamashita, Tomoki Koriyama, Yuki Saito, Shinnosuke Takamichi, Yusuke Ijima, Ryo Masumura, Hiroshi Saruwatari
| Challenge: | Experimental evaluation results show that rich annotations enhance the reproducibility of paralinguistic features of synthetic speech. |
| Approach: | They investigate the effectiveness of using rich annotations in deep neural network-based statistical speech synthesis. |
| Outcome: | The proposed method improves reproducibility of paralinguistic features of synthetic speech . the corpus of spontaneous Japanese (CSJ) has large annotations on paralinguistic and nonlinguistic features . |
Adversarial Training for Multi-task and Multi-lingual Joint Modeling of Utterance Intent Classification (D18-1)
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| Challenge: | In multi-task and multi-lingual joint modeling, common knowledge can be efficiently utilized among multiple tasks or multiple languages. |
| Approach: | They propose to introduce language-specific adversarial networks and task-specific language adversarials to purge the task or language dependencies of shared networks. |
| Outcome: | The proposed method is demonstrated using Japanese and English data sets for three different utterance intent classification tasks. |