Alignment-Based Decoding Policy for Low-Latency and Anticipation-Free Neural Japanese Input Method Editors

Armin Sarhangzadeh, Taro Watanabe

Abstract
Japanese input method editors (IMEs) are essential tools for inputting Japanese text using a limited set of characters such as the kana syllabary. However, despite their importance, the potential of newer attention-based encoder-decoder neural networks, such as Transformer, has not yet been fully explored for IMEs due to their high computational cost and low-quality intermediate output in simultaneous settings, leading to high latencies. In this work, we propose a simple decoding policy to enable the use of attention-based encoder-decoder networks for simultaneous kana-kanji conversion in the context of Japanese IMEs inspired by simultaneous machine translation (SimulMT). We demonstrate that simply decoding by explicitly considering the word boundaries achieves a fairly strong quality-latency trade-off, as it can be seen as equivalent to performing decoding on aligned prefixes and thus achieving an incremental anticipation-free conversion. We further show how such a policy can be applied in practice to achieve high-quality conversions with minimal computational overhead. Our experiments show that our approach can achieve a noticeably better quality-latency trade-off compared to the baselines, while also being a more practical approach due to its ability to directly handle streaming input. Our code is available at https://anonymous.4open.science/r/transformer_ime-D327.
Anthology ID:
2024.findings-acl.479
Volume:
Findings of the Association for Computational Linguistics: ACL 2024
Month:
August
Year:
2024
Address:
Bangkok, Thailand
Editors:
Lun-Wei Ku, Andre Martins, Vivek Srikumar
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
8043–8054
Language:
URL:
https://aclanthology.org/2024.findings-acl.479
DOI:
10.18653/v1/2024.findings-acl.479
Bibkey:
Cite (ACL):
Armin Sarhangzadeh and Taro Watanabe. 2024. Alignment-Based Decoding Policy for Low-Latency and Anticipation-Free Neural Japanese Input Method Editors. In Findings of the Association for Computational Linguistics: ACL 2024, pages 8043–8054, Bangkok, Thailand. Association for Computational Linguistics.
Cite (Informal):
Alignment-Based Decoding Policy for Low-Latency and Anticipation-Free Neural Japanese Input Method Editors (Sarhangzadeh & Watanabe, Findings 2024)
Copy Citation:
PDF:
https://preview.aclanthology.org/dois-2013-emnlp/2024.findings-acl.479.pdf
PDF Search