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Register a new userCSECU-DSG at SemEval-2021 Task 1: Fusion of Transformer Models for Lexical Complexity Prediction
CSECU-DSG في مهمة Semeval-2021 1: الانصهار من نماذج المحولات لتنبؤ التعقيد المعجمي
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تنقل تنبؤ التعقيد المعجمي (LCP) باحسن مستوى تعقيد رمز رمزي أو مجموعة من الرموز في جملة.يلعب دورا حيويا في تحسين مهام NLP المختلفة بما في ذلك التبسيط المعجمي والترجمات وتوليد النص.ومع ذلك، فإن المعنى المتعدد لكلمة في ظروف متعددة، وهيكل مجمع نحوي، والاعتماد المتبادل للكلمات في جملة تجعل من الصعب تقدير التعقيد المعجمي.لمعالجة هذه التحديات، قدمت مهمة Semeval-2021 1 مهمة مشتركة تركز على LCP وتعرض هذه الورقة مشاركتنا في هذه المهمة.اقترحنا نهجا قائم على المحولات مع انحدار زوج الجملة.نحن عملنا نماذج محول صعبة ضبطها.بما في ذلك بيرت وروبرتا لتدريب نموذجنا وصماماتها المتوقعة لتقدير التعقيد.توضح النتائج التجريبية أن طريقةنا المقترحة تحققت أداء تنافسي مقارنة بنظم المشاركين.
Lexical complexity prediction (LCP) conveys the anticipation of the complexity level of a token or a set of tokens in a sentence. It plays a vital role in the improvement of various NLP tasks including lexical simplification, translations, and text generation. However, multiple meaning of a word in multiple circumstances, grammatical complex structure, and the mutual dependency of words in a sentence make it difficult to estimate the lexical complexity. To address these challenges, SemEval-2021 Task 1 introduced a shared task focusing on LCP and this paper presents our participation in this task. We proposed a transformer-based approach with sentence pair regression. We employed two fine-tuned transformer models. Including BERT and RoBERTa to train our model and fuse their predicted score to the complexity estimation. Experimental results demonstrate that our proposed method achieved competitive performance compared to the participants' systems.
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