An Efficient System for Grammatical Error Correction on Mobile Devices (Research Paper Walkthrough)

An Efficient System for Grammatical Error Correction on Mobile Devices (Research Paper Walkthrough)

May 24, 2021
|
28 views
Details
#nlp #grammarcorrection #machinelearning This research proposes a pipeline for On-device Grammar Error Correction (GEC). The pipeline consists of 4 components - spell checker, DNN, N-gram check, Rule Corrector. They also propose many optimization techniques resulting in very low sized yet accurate model. Watch to know more :) ⏩ Abstract: As per the analysis by lemongrad, among 1,121 million English speakers globally, there are 743 million nonnative and 378 million native English speakers (7:3 Ratio). With the increasing number of non-native English speakers, there has been a lot of ongoing research on automated Grammatical Error Correction (GEC). Despite the advances in this field, a GEC system for mobile devices with a low model size, quick inference time and high accuracy is a pressing need. Existing solutions for mobile devices are server-based, which poses a potential risk of data privacy to the user. To this end, we propose an on-device hybrid system which consists of various components, such as preprocessor with spell-checker, Deep Neural Network (DNN) model, N-gram language model, rule-corrector for GEC. User's input text is passed through the proposed system sequentially to get the grammatically correct, contextually enhanced and profanity checked output sentence. Our novel system is at par with other available models for mobile devices with inference time of 95ms, module size of 11.6 MB and F 0.5 score of 52.3 on CoNLL-2014 (test) Please feel free to share out the content and subscribe to my channel :) ⏩ Subscribe - https://youtube.com/channel/UCoz8NrwgL7U9535VNc0mRPA?sub_confirmation=1 ⏩ OUTLINE: 0:00 - Abstract & Background 02:46 - Dataset generation for Grammar correction 04:42 - Spell-Checker - Component 1 06:12 - DNN Model (Sequence-to-Sequence & Sequence Tagger Lite) - Component 2 12:57 - Native Inference Engine 13:38 - N-gram filter - Component 3 18:43 - Rule-corrector - Component 4 19:53 - My thoughts ⏩ Paper Title: An Efficient System for Grammatical Error Correction on Mobile Devices ⏩ Paper: https://ieeexplore.ieee.org/document/9364435 ⏩ Author: Sourabh Vasant Gothe; Sushant Dogra; Mritunjai Chandra; Chandramouli Sanchi; Barath Raj Kandur Raja ⏩ Organisation: Samsung R&D Bangalore ********************************************* If you want to support me financially which totally optional and voluntary :) ❤️ You can consider buying me chai ( because i don't drink coffee :) ) at https://www.buymeacoffee.com/TechvizCoffee ********************************************* ⏩ Youtube - https://www.youtube.com/c/TechVizTheDataScienceGuy ⏩ Blog - https://prakhartechviz.blogspot.com ⏩ LinkedIn - https://linkedin.com/in/prakhar21 ⏩ Medium - https://medium.com/@prakhar.mishra ⏩ GitHub - https://github.com/prakhar21 ⏩ Twitter - https://twitter.com/rattller ********************************************* Tools I use for making videos :) ⏩ iPad - https://tinyurl.com/y39p6pwc ⏩ Apple Pencil - https://tinyurl.com/y5rk8txn ⏩ GoodNotes - https://tinyurl.com/y627cfsa #techviz #datascienceguy #seq2seq #lstm

0:00 Abstract & Background 02:46 Dataset generation for Grammar correction 04:42 Spell-Checker - Component 1 06:12 DNN Model (Sequence-to-Sequence & Sequence Tagger Lite) - Component 2 12:57 Native Inference Engine 13:38 N-gram filter - Component 3 18:43 Rule-corrector - Component 4 19:53 My thoughts
Comments
loading...