12 citations · 32 across the 11 of their papers we have counts for
14 papers
Single Model Ensemble for Subword Regularized Models in Low-Resource Machine Translation
Sho Takase, Tatsuya Hiraoka, Naoaki Okazaki
Subword regularizations use multiple subword segmentations during training to improve the robustness of neural machine translation models. In previous subword regularizations, we u…
Interpretability for Language Learners Using Example-Based Grammatical Error Correction
Masahiro Kaneko, Sho Takase, Ayana Niwa +1
Grammatical Error Correction (GEC) should not focus only on high accuracy of corrections but also on interpretability for language learning. However, existing neural-based GEC mode…
ExtraPhrase: Efficient Data Augmentation for Abstractive Summarization
Mengsay Loem, Sho Takase, Masahiro Kaneko +1
Neural models trained with large amount of parallel data have achieved impressive performance in abstractive summarization tasks. However, large-scale parallel corpora are expensiv…
Joint Optimization of Tokenization and Downstream Model
Tatsuya Hiraoka, Sho Takase, Kei Uchiumi +2
Since traditional tokenizers are isolated from a downstream task and model, they cannot output an appropriate tokenization depending on the task and model, although recent studies…
Rethinking Perturbations in Encoder-Decoders for Fast Training
Sho Takase, Shun Kiyono
We often use perturbations to regularize neural models. For neural encoder-decoders, previous studies applied the scheduled sampling (Bengio et al., 2015) and adversarial perturbat…
Multi-Task Learning for Cross-Lingual Abstractive Summarization
Sho Takase, Naoaki Okazaki
We present a multi-task learning framework for cross-lingual abstractive summarization to augment training data. Recent studies constructed pseudo cross-lingual abstractive summari…