Shun Kiyono

An Empirical Study of Incorporating Pseudo Data into Grammatical Error Correction

2019 · arXiv (Cornell University)

The incorporation of pseudo data in the training of grammatical error correction models has been one of the main factors in improving the performance of such models. However, consensus is lacking on experimental configurations, namely, …

ESPnet-ST: All-in-One Speech Translation Toolkit

2020 · arXiv (Cornell University)

We present ESPnet-ST, which is designed for the quick development of speech-to-speech translation systems in a single framework. ESPnet-ST is a new project inside end-to-end speech processing toolkit, ESPnet, which integrates or newly implements automatic …

A Self-Refinement Strategy for Noise Reduction in Grammatical Error Correction

2020 · arXiv (Cornell University)

Existing approaches for grammatical error correction (GEC) largely rely on supervised learning with manually created GEC datasets. However, there has been little focus on verifying and ensuring the quality of the datasets, and on how …

Rethinking Perturbations in Encoder-Decoders for Fast Training

2021 · arXiv (Cornell University)

We often use perturbations to regularize neural models. For neural encoder-decoders, previous studies applied the scheduled sampling (Bengio et al., 2015) and adversarial perturbations (Sato et al., 2019) as perturbations but these methods require considerable …

Effective Adversarial Regularization for Neural Machine Translation

2019

A regularization technique based on adversarial perturbation, which was initially developed in the field of image processing, has been successfully applied to text classification tasks and has yielded attractive improvements. We aim to further leverage …