Alignment-Based Decoding Policy for Low-Latency and Anticipation-Free Neural Japanese Input Method Editors
Armin Sarhangzadeh, Taro Watanabe · 2024
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 encoderdecoder 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.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