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Register a new userLinguistic Knowledge in Multilingual Grapheme-to-Phoneme Conversion
المعرفة اللغوية في التحويل متعدد اللغات
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وثائق هذه الورقة نهج فريق Linguistics UBC في مهمة SIGMORPHON 2021 Graphem-To-PhoneMe المشتركة، والتركيز على إعداد الموارد المنخفضة.توسع أنظمتنا نموذج الأساس مع تعديلات بسيطة على علم بنية مقطع لفظي وتحليل الأخطاء.يبين التحقيق المتعمق في تنبؤات مجموعة الاختبار أن أفضل طرازنا يصحح عددا كبيرا من الأخطاء مقارنة بالتنبؤ الأساسي، حيث فهناك جميع التقديمات الأخرى.تحقق نتائجنا الرأي الذي يمكن أن يؤدي تحليل الأخطاء الدقيق مع المعرفة اللغوية إلى نمذجة حسابية أكثر فعالية.
This paper documents the UBC Linguistics team's approach to the SIGMORPHON 2021 Grapheme-to-Phoneme Shared Task, concentrating on the low-resource setting. Our systems expand the baseline model with simple modifications informed by syllable structure and error analysis. In-depth investigation of test-set predictions shows that our best model rectifies a significant number of mistakes compared to the baseline prediction, besting all other submissions. Our results validate the view that careful error analysis in conjunction with linguistic knowledge can lead to more effective computational modeling.
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This paper describes the submission by the team from the Department of Computational Linguistics, Zurich University, to the Multilingual Grapheme-to-Phoneme Conversion (G2P) Task 1 of the SIGMORPHON 2021 challenge in the low and medium settings. The
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We describe the NUIG solution for IWPT 2021 Shared Task of Enhanced Dependency (ED) parsing in multiple languages. For this shared task, we propose and evaluate an End-to-end Seq2seq mBERT-based ED parser which predicts the ED-parse tree of a given i
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We describe three baseline beating systems for the high-resource English-only sub-task of the SIGMORPHON 2021 Shared Task 1: a small ensemble that Dialpad's speech recognition team uses internally, a well-known off-the-shelf model, and a larger ensem
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