111 citations · 552 across the 49 of their papers we have counts for
11 papers · 1 filter
Improving Neural Machine Translation through Phrase-based Forced Decoding
Jingyi Zhang, Masao Utiyama, Eiichro Sumita +2
Compared to traditional statistical machine translation (SMT), neural machine translation (NMT) often sacrifices adequacy for the sake of fluency. We propose a method to combine th…
Sequence-to-Sequence ASR Optimization via Reinforcement Learning
Andros Tjandra, Sakriani Sakti, Satoshi Nakamura
Despite the success of sequence-to-sequence approaches in automatic speech recognition (ASR) systems, the models still suffer from several problems, mainly due to the mismatch betw…
Attention-based Wav2Text with Feature Transfer Learning
Andros Tjandra, Sakriani Sakti, Satoshi Nakamura
Conventional automatic speech recognition (ASR) typically performs multi-level pattern recognition tasks that map the acoustic speech waveform into a hierarchy of speech units. But…
Transcribing Against Time
Matthias Sperber, Graham Neubig, Jan Niehues +2
We investigate the problem of manually correcting errors from an automatic speech transcript in a cost-sensitive fashion. This is done by specifying a fixed time budget, and then a…
Listening while Speaking: Speech Chain by Deep Learning
Andros Tjandra, Sakriani Sakti, Satoshi Nakamura
Despite the close relationship between speech perception and production, research in automatic speech recognition (ASR) and text-to-speech synthesis (TTS) has progressed more or le…
An Empirical Study of Mini-Batch Creation Strategies for Neural Machine Translation
Makoto Morishita, Yusuke Oda, Graham Neubig +3
Training of neural machine translation (NMT) models usually uses mini-batches for efficiency purposes. During the mini-batched training process, it is necessary to pad shorter sent…
Gated Recurrent Neural Tensor Network
Andros Tjandra, Sakriani Sakti, Ruli Manurung +2
Recurrent Neural Networks (RNNs), which are a powerful scheme for modeling temporal and sequential data need to capture long-term dependencies on datasets and represent them in hid…
Analysis of the Effect of Dependency Information on Predicate-Argument Structure Analysis and Zero Anaphora Resolution
Koichiro Yoshino, Shinsuke Mori, Satoshi Nakamura
This paper investigates and analyzes the effect of dependency information on predicate-argument structure analysis (PASA) and zero anaphora resolution (ZAR) for Japanese, and shows…
Compressing Recurrent Neural Network with Tensor Train
Andros Tjandra, Sakriani Sakti, Satoshi Nakamura
Recurrent Neural Network (RNN) are a popular choice for modeling temporal and sequential tasks and achieve many state-of-the-art performance on various complex problems. However, m…
Local Monotonic Attention Mechanism for End-to-End Speech and Language Processing
Andros Tjandra, Sakriani Sakti, Satoshi Nakamura
Recently, encoder-decoder neural networks have shown impressive performance on many sequence-related tasks. The architecture commonly uses an attentional mechanism which allows the…
Neural Machine Translation via Binary Code Prediction
Yusuke Oda, Philip Arthur, Graham Neubig +2
In this paper, we propose a new method for calculating the output layer in neural machine translation systems. The method is based on predicting a binary code for each word and can…