Generative Input: Towards Next-Generation Input Methods Paradigm
This addresses the need for more robust and personalized input methods for users, though it appears incremental as it builds on existing generative models.
The paper tackles the problem of improving Chinese input methods by proposing a generative input paradigm that handles all input scenarios and leverages user feedback for personalization, achieving state-of-the-art performance in the Full-mode Key-sequence to Characters task and surpassing GPT-4 in intelligent association and conversational assistance.
Since the release of ChatGPT, generative models have achieved tremendous success and become the de facto approach for various NLP tasks. However, its application in the field of input methods remains under-explored. Many neural network approaches have been applied to the construction of Chinese input method engines(IMEs).Previous research often assumed that the input pinyin was correct and focused on Pinyin-to-character(P2C) task, which significantly falls short of meeting users' demands. Moreover, previous research could not leverage user feedback to optimize the model and provide personalized results. In this study, we propose a novel Generative Input paradigm named GeneInput. It uses prompts to handle all input scenarios and other intelligent auxiliary input functions, optimizing the model with user feedback to deliver personalized results. The results demonstrate that we have achieved state-of-the-art performance for the first time in the Full-mode Key-sequence to Characters(FK2C) task. We propose a novel reward model training method that eliminates the need for additional manual annotations and the performance surpasses GPT-4 in tasks involving intelligent association and conversational assistance. Compared to traditional paradigms, GeneInput not only demonstrates superior performance but also exhibits enhanced robustness, scalability, and online learning capabilities.