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Register a new userPredicting the Success of Domain Adaptation in Text Similarity
التنبؤ بنجاح تكيف المجال في تشابه النص
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طرق التعلم، وخاصة تكيف المجال، تساعد في استغلال البيانات المسمى في مجال واحد لتحسين أداء مهمة معينة في مجال آخر.ومع ذلك، لا يزال من غير الواضح العوامل التي تؤثر على نجاح تكيف المجال.نماذج الورق هذه النجاح ونجاح واختيار المجالات المصدر الأكثر ملاءمة بين العديد من المرشحين في تشابه النص.نستخدم معلومات المجال الوصفية ومقاييس التشابه عبر المجال كيزات تنبؤية.في الغالب إيجابية، تشير النتائج أيضا إلى بعض المجالات حيث كان من الصعب التنبؤ بنجاح التكيف.
Transfer learning methods, and in particular domain adaptation, help exploit labeled data in one domain to improve the performance of a certain task in another domain. However, it is still not clear what factors affect the success of domain adaptation. This paper models adaptation success and selection of the most suitable source domains among several candidates in text similarity. We use descriptive domain information and cross-domain similarity metrics as predictive features. While mostly positive, the results also point to some domains where adaptation success was difficult to predict.
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