Chinese Pinyin Aided IME, Input What You Have Not Keystroked Yet

Huang, Yafang; Zhao, Hai

doi:10.18653/v1/d18-1321

Cited by 11 publications

(2 citation statements)

References 18 publications

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“…Chen et al [4] were the first to use a neural network to integrate a back-off n-gram language model into a Pinyin input method. Following the successful application of attention mechanisms [8], [14], researchers began to use neural machine translation frameworks to translate from Pinyin to Chinese [3], [5], [6]. To minimize user tapping and improve their experiences, neural-network-based IMEs must take full advantage of the contextual information of user input to predict behaviors [5] while learning new vocabularies and tracking adaptations to user input behaviors [3].…”

Section: B Neural-network-based Pinyin Imesmentioning

confidence: 99%

Adaptive Chinese Pinyin IME for Most Similar Representation

2022

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Many neural-network approaches are used for Pinyin-to-character (P2C) conversion in Chinese input method engines (IMEs). However, in previous research, the conversion efficiency of neuralnetwork P2C models depends on the training of the data. Unfortunately, neural networks cannot maintain high performance with conversions across users and domains. In this study, we propose a method for improving the efficiency of model conversion and tracking user behavior based on dynamic storage and representations that can be updated using historical information from user input. Our experimental results show that our technique tracks user behavior and has strong domain adaptability without requiring additional training. For the cross-domain datasets Touchpal, cMedQA1.0, CAIL2019, compared with the direct use of neural network, its indicators, Top-1 MIU-Acc, CA and KySS, are improved by at least 20.0 8.1 18.3%, respectively, and the results are close to the in-domain training of the model. Furthermore, compared with the traditional methods On-OMWA and Google IME, this method improves at least 7.8 2.0 11.9% and 3.2 0.7 13.9% in Top-1 MIU-Acc, CA and Kyss, respectively. This demonstrates that the proposed method is superior to existing models in terms of conversion accuracy and generality, and can point a new path for P2C platforms.

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Section: B Neural-network-based Pinyin Imesmentioning

confidence: 99%

Adaptive Chinese Pinyin IME for Most Similar Representation

2022

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“…For years, vendors’ endeavors are limited to the kernel including a built-in vocabulary. 35 – 38 Increasing the underlying word length seems friendly to TLK. However, to our knowledge, it receives limited attention in the clinical setting.…”

Section: Introductionmentioning

confidence: 99%

Using less keystrokes to achieve high top-1 accuracy in Chinese clinical text entry

Zhou

et al. 2023

DIGITAL HEALTH

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Background As a routine task, physicians spend substantial time and keystrokes on text entry. Documentation burden is increasingly associated with physician burnout. Predicting at top-1 with less keystrokes (TLKs) is a hot topic for smart text entry. In Western countries, contextual autocomplete is deployed to alleviate the burden. Chinese text entry is intercepted by input method engines (IMEs), which cut off suggestions from electronic health records (EHRs). Objective To explore a user-friendly approach to make text entry easier and faster for Chinese physicians. Methods Physicians were shadowed to uncover the real-word input behaviors. System logs were collected for behavior validation and then used for context-based learning. An in-line web-based popup layer was proposed to hold the best suggestion from EHRs. Keystrokes per character and TLK rate were evaluated quantitatively. Questionnaires were used for qualitative assessment. Nine hundred fifty-two physicians were enrolled in a field testing. Results 14 facilitators and 17 barriers related to IMEs were identified after shadowing. With system logs, physicians tended to split long words into short units, which were 1–4 in length. 81.7% of these units were disyllables. Compared to the control group, the intervention group improved TLK rate by 40.3% ( p < .0001), and reduced keystrokes per character by 48.3% ( p < .0001). Survey results also promised positive feedback from physicians. Conclusions Keystroke burden and frequent choice reaction time challenge Chinese physicians for text entry. The proposed system demonstrates an approach to alleviate the burden. Contextual information is easily retrieved and it further helps improve the top-1 accuracy, with a smaller number of keystrokes. While positive feedback is received, it promises a benefit to protect patient privacy.

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Cross Aggregation of Multi-head Attention for Neural Machine Translation

Cao

Zhao

2019

Natural Language Processing and Chinese Computing

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Chinese Pinyin Aided IME, Input What You Have Not Keystroked Yet

Cited by 11 publications

References 18 publications

Adaptive Chinese Pinyin IME for Most Similar Representation

Adaptive Chinese Pinyin IME for Most Similar Representation

Using less keystrokes to achieve high top-1 accuracy in Chinese clinical text entry

Cross Aggregation of Multi-head Attention for Neural Machine Translation

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