على الرغم من أن تصحيح الخطأ النحوي (GEC) قد حقق أداء جيدا على النصوص التي كتبها المتعلمون من اللغة الإنجليزية كلغة ثانية، فإن الأداء على نطاقات كثافة الأخطاء المنخفضة حيث لا يزال من الممكن تحسين النصوص عن طريق مكبرات الصوت الإنجليزية من مستويات مختلفة من الكفاءة.في هذه الورقة، نقترح نهجا للتعلم المتعاقيض لتشجيع نموذج GEC لتعيين احتمال أعلى من الجملة الصحيحة مع تقليل احتمالية جمل غير صحيحة أن النموذج يميل إلى توليدها، وذلك لتحسين دقة النموذج.تظهر النتائج التجريبية أن نهجنا يحسن بشكل كبير أداء نماذج GEC في مجالات كثافة خطأ منخفضة، عند تقييمه على مجموعة بيانات CWEB القياسية.
Although grammatical error correction (GEC) has achieved good performance on texts written by learners of English as a second language, performance on low error density domains where texts are written by English speakers of varying levels of proficiency can still be improved. In this paper, we propose a contrastive learning approach to encourage the GEC model to assign a higher probability to a correct sentence while reducing the probability of incorrect sentences that the model tends to generate, so as to improve the accuracy of the model. Experimental results show that our approach significantly improves the performance of GEC models in low error density domains, when evaluated on the benchmark CWEB dataset.
References used
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