Tomohiro Tanaka


2020

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Memory Attentive Fusion: External Language Model Integration for Transformer-based Sequence-to-Sequence Model
Mana Ihori | Ryo Masumura | Naoki Makishima | Tomohiro Tanaka | Akihiko Takashima | Shota Orihashi
Proceedings of the 13th International Conference on Natural Language Generation

This paper presents a novel fusion method for integrating an external language model (LM) into the Transformer based sequence-to-sequence (seq2seq) model. While paired data are basically required to train the seq2seq model, the external LM can be trained with only unpaired data. Thus, it is important to leverage memorized knowledge in the external LM for building the seq2seq model, since it is hard to prepare a large amount of paired data. However, the existing fusion methods assume that the LM is integrated with recurrent neural network-based seq2seq models instead of the Transformer. Therefore, this paper proposes a fusion method that can explicitly utilize network structures in the Transformer. The proposed method, called memory attentive fusion, leverages the Transformer-style attention mechanism that repeats source-target attention in a multi-hop manner for reading the memorized knowledge in the LM. Our experiments on two text-style conversion tasks demonstrate that the proposed method performs better than conventional fusion methods.

2018

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Neural Dialogue Context Online End-of-Turn Detection
Ryo Masumura | Tomohiro Tanaka | Atsushi Ando | Ryo Ishii | Ryuichiro Higashinaka | Yushi Aono
Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue

This paper proposes a fully neural network based dialogue-context online end-of-turn detection method that can utilize long-range interactive information extracted from both speaker’s utterances and collocutor’s utterances. The proposed method combines multiple time-asynchronous long short-term memory recurrent neural networks, which can capture speaker’s and collocutor’s multiple sequential features, and their interactions. On the assumption of applying the proposed method to spoken dialogue systems, we introduce speaker’s acoustic sequential features and collocutor’s linguistic sequential features, each of which can be extracted in an online manner. Our evaluation confirms the effectiveness of taking dialogue context formed by the speaker’s utterances and collocutor’s utterances into consideration.

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Multi-task and Multi-lingual Joint Learning of Neural Lexical Utterance Classification based on Partially-shared Modeling
Ryo Masumura | Tomohiro Tanaka | Ryuichiro Higashinaka | Hirokazu Masataki | Yushi Aono
Proceedings of the 27th International Conference on Computational Linguistics

This paper is an initial study on multi-task and multi-lingual joint learning for lexical utterance classification. A major problem in constructing lexical utterance classification modules for spoken dialogue systems is that individual data resources are often limited or unbalanced among tasks and/or languages. Various studies have examined joint learning using neural-network based shared modeling; however, previous joint learning studies focused on either cross-task or cross-lingual knowledge transfer. In order to simultaneously support both multi-task and multi-lingual joint learning, our idea is to explicitly divide state-of-the-art neural lexical utterance classification into language-specific components that can be shared between different tasks and task-specific components that can be shared between different languages. In addition, in order to effectively transfer knowledge between different task data sets and different language data sets, this paper proposes a partially-shared modeling method that possesses both shared components and components specific to individual data sets. We demonstrate the effectiveness of proposed method using Japanese and English data sets with three different lexical utterance classification tasks.