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Register a new userNew Domain, Major Effort? How Much Data is Necessary to Adapt a Temporal Tagger to the Voice Assistant Domain
مجال جديد، جهد كبير؟ما مقدار البيانات الضرورية لتكييف علامة زمنية إلى مجال مساعد الصوت
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تعتبر العلامات الموثوقة للتعبيرات الزمنية (TES، على سبيل المثال، كتاب طاولة في L'Osteria مساء الأحد) هو الشرط المركزي للمساعدين الصوتيين (VAS).ومع ذلك، هناك ندرة الموارد والأنظمة لنطاق VA، حيث يتم تدريب التقنيص الزمني المتاحين علنا فقط على مجالات مختلفة إلى حد كبير، مثل الأخبار والنص السريري.نظرا لأن تكلفة التسجيل في مجموعات البيانات الكبيرة عبارة عن محظور، فإننا نحقق في المفاضلة بين البيانات والأداء داخل المجال في DA-Time، وهو Tagger الزمني الهجين للمجال الإنجليزي VA الذي يجمع بين الهندسة المعمارية العصبية للاعتراف القوي، مع محللباس te نومي.نجد أن التعلم النقل يقطع شوطا طويلا حتى مع وجود 25 جمل داخل المجال: يؤدي DA-Time في حالة الفن في مجال الأخبار، وتفوقه بشكل كبير على نطاق VA.
Reliable tagging of Temporal Expressions (TEs, e.g., Book a table at L'Osteria for Sunday evening) is a central requirement for Voice Assistants (VAs). However, there is a dearth of resources and systems for the VA domain, since publicly-available temporal taggers are trained only on substantially different domains, such as news and clinical text. Since the cost of annotating large datasets is prohibitive, we investigate the trade-off between in-domain data and performance in DA-Time, a hybrid temporal tagger for the English VA domain which combines a neural architecture for robust TE recognition, with a parser-based TE normalizer. We find that transfer learning goes a long way even with as little as 25 in-domain sentences: DA-Time performs at the state of the art on the news domain, and substantially outperforms it on the VA domain.
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