111 citations · 552 across the 49 of their papers we have counts for
12 papers · 1 filter
Optimization of Information-Seeking Dialogue Strategy for Argumentation-Based Dialogue System
Hisao Katsumi, Takuya Hiraoka, Koichiro Yoshino +4
Argumentation-based dialogue systems, which can handle and exchange arguments through dialogue, have been widely researched. It is required that these systems have sufficient suppo…
Another Diversity-Promoting Objective Function for Neural Dialogue Generation
Ryo Nakamura, Katsuhito Sudoh, Koichiro Yoshino +1
Although generation-based dialogue systems have been widely researched, the response generations by most existing systems have very low diversities. The most likely reason for this…
Multi-Source Neural Machine Translation with Data Augmentation
Yuta Nishimura, Katsuhito Sudoh, Graham Neubig +1
Multi-source translation systems translate from multiple languages to a single target language. By using information from these multiple sources, these systems achieve large gains…
End-to-End Feedback Loss in Speech Chain Framework via Straight-Through Estimator
Andros Tjandra, Sakriani Sakti, Satoshi Nakamura
The speech chain mechanism integrates automatic speech recognition (ASR) and text-to-speech synthesis (TTS) modules into a single cycle during training. In our previous work, we ap…
Training Neural Machine Translation using Word Embedding-based Loss
Katsuki Chousa, Katsuhito Sudoh, Satoshi Nakamura
In neural machine translation (NMT), the computational cost at the output layer increases with the size of the target-side vocabulary. Using a limited-size vocabulary instead may c…
Multi-scale Alignment and Contextual History for Attention Mechanism in Sequence-to-sequence Model
Andros Tjandra, Sakriani Sakti, Satoshi Nakamura
A sequence-to-sequence model is a neural network module for mapping two sequences of different lengths. The sequence-to-sequence model has three core modules: encoder, decoder, and…
Multi-Source Neural Machine Translation with Missing Data
Yuta Nishimura, Katsuhito Sudoh, Graham Neubig +1
Multi-source translation is an approach to exploit multiple inputs (e.g. in two different languages) to increase translation accuracy. In this paper, we examine approaches for mult…
Guiding Neural Machine Translation with Retrieved Translation Pieces
Jingyi Zhang, Masao Utiyama, Eiichro Sumita +2
One of the difficulties of neural machine translation (NMT) is the recall and appropriate translation of low-frequency words or phrases. In this paper, we propose a simple, fast, a…
Machine Speech Chain with One-shot Speaker Adaptation
Andros Tjandra, Sakriani Sakti, Satoshi Nakamura
In previous work, we developed a closed-loop speech chain model based on deep learning, in which the architecture enabled the automatic speech recognition (ASR) and text-to-speech…
Interactive Image Manipulation with Natural Language Instruction Commands
Seitaro Shinagawa, Koichiro Yoshino, Sakriani Sakti +2
We propose an interactive image-manipulation system with natural language instruction, which can generate a target image from a source image and an instruction that describes the d…
Structured-based Curriculum Learning for End-to-end English-Japanese Speech Translation
Takatomo Kano, Sakriani Sakti, Satoshi Nakamura
Sequence-to-sequence attentional-based neural network architectures have been shown to provide a powerful model for machine translation and speech recognition. Recently, several wo…
Tensor Decomposition for Compressing Recurrent Neural Network
Andros Tjandra, Sakriani Sakti, Satoshi Nakamura
In the machine learning fields, Recurrent Neural Network (RNN) has become a popular architecture for sequential data modeling. However, behind the impressive performance, RNNs requ…