{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/context-based-approach-for-second-language","title":"Context Based Approach for Second Language Acquisition","arxiv_id":null,"date":"2018-06-01","proceeding":"WS 2018 6","authors":["Nihal V. Nayak","Arjun R. Rao"],"abstract":"SLAM 2018 focuses on predicting a student{'}s mistake while using the Duolingo application. In this paper, we describe the system we developed for this shared task. Our system uses a logistic regression model to predict the likelihood of a student making a mistake while answering an exercise on Duolingo in all three language tracks - English/Spanish (en/es), Spanish/English (es/en) and French/English (fr/en). We conduct an ablation study with several features during the development of this system and discover that context based features plays a major role in language acquisition modeling. Our model beats Duolingo{'}s baseline scores in all three language tracks (AUROC scores for en/es = 0.821, es/en = 0.790 and fr/en = 0.812). Our work makes a case for providing favourable textual context for students while learning second language.","url_abs":"https://aclanthology.org/W18-0524","url_pdf":"https://aclanthology.org/W18-0524.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"context-based-approach-for-second-language","repo_url":"https://github.com/arjun-rao/slam18","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"context-based-approach-for-second-language","repo_url":"https://github.com/iampuntre/slam18","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"language-acquisition","task_name":"Language Acquisition"},{"task_slug":"es-en","task_name":"es-en"},{"task_slug":"fr-en","task_name":"fr-en"}],"methods":[{"method_slug":"logistic-regression","method_name":"Logistic Regression"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/language-acquisition-on-slam-2018","task":"Language Acquisition","dataset":"SLAM 2018","model":"Context Based Model","rank_in_archive_order":1,"of":1,"metrics":{"AUC":"0.821"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}