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Register a new userMulti-modal Retrieval of Tables and Texts Using Tri-encoder Models
استرجاع متعددة الوسائط للجداول والنصوص باستخدام نماذج Tri-Encoder
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تفتح الأسئلة الاستخراجية المفتوحة الإجابة تعمل بشكل جيد على البيانات النصية من خلال استرداد النصوص المرشحة أولا ثم استخراج الإجابة من هؤلاء المرشحين. ومع ذلك، لا يمكن الإجابة على بعض الأسئلة بالنص وحدها ولكنها تتطلب معلومات مخزنة في الجداول. في هذه الورقة، نقدم نهج لاستعادة كلا النصين والجداول ذات الصلة بالسؤال من خلال ترميز النصوص والطاولات والأسئلة في مساحة متجه واحدة. تحقيقا لهذه الغاية، نقوم بإنشاء مجموعة بيانات جديدة متعددة الوسائط بناء على مجموعات بيانات النصوص والجدول من العمل ذي الصلة ومقارنة أداء استرجاع مخطط ترميز مختلفة. نجد أن تضمين ناقلات كثيفة نماذج المحولات تتفوق على تضمين متفرق في أربع مجموعات من مجموعات بيانات تقييم. مقارنة نماذج تضمين كثيفة مختلفة، تزيد TRI-Encoders مع ترميز واحد لكل سؤال ونص وجدول أداء استرجاع مقارنة بالتشفيات الثنائية مع ترميز واحد للحصول على سؤال واحد لكل من النص والجداول. نطلق سراح مجموعة بيانات متعددة الوسائط التي تم إنشاؤها حديثا للمجتمع بحيث يمكن استخدامها للتدريب والتقييم.
Open-domain extractive question answering works well on textual data by first retrieving candidate texts and then extracting the answer from those candidates. However, some questions cannot be answered by text alone but require information stored in tables. In this paper, we present an approach for retrieving both texts and tables relevant to a question by jointly encoding texts, tables and questions into a single vector space. To this end, we create a new multi-modal dataset based on text and table datasets from related work and compare the retrieval performance of different encoding schemata. We find that dense vector embeddings of transformer models outperform sparse embeddings on four out of six evaluation datasets. Comparing different dense embedding models, tri-encoders with one encoder for each question, text and table increase retrieval performance compared to bi-encoders with one encoder for the question and one for both text and tables. We release the newly created multi-modal dataset to the community so that it can be used for training and evaluation.
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