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Register a new userUnsupervised Representation Disentanglement of Text: An Evaluation on Synthetic Datasets
تمثيل تمثيل غير مؤظفي النص: تقييم في مجموعات البيانات الاصطناعية
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لتسليط الضوء على تحديات تحقيق تنصيب تمثيل المجال النصي في إعداد غير محدد، في هذه الورقة نقوم بتحديد مجموعة تمثيلية من النماذج المطبقة بنجاح من مجال الصورة.نحن نقيم هذه النماذج على 6 مقاييس DEFENTANCE، وكذلك على مهام التصنيف المصب والمهماطوب.لتسهيل التقييم، نقترح اثنين من مجموعات البيانات الاصطناعية مع عوامل تابعة معروفة.تبرز تجاربنا الفجوة الموجودة في المجال النصي وتوضح أن بعض العناصر مثل التمثيل SPARSITY (كحيز حثي)، أو اقتران التمثيل مع وحدة فك الترميز يمكن أن يؤثر على deventanglement.إلى حد ما من معرفتنا، فإن عملنا هو المحاولة الأولى لتقاطع تحسس وتصوير تمثيل غير مدهش، ويوفر الإطار التجريبي ومجموعات البيانات لفحص التطورات المستقبلية في هذا الاتجاه.
To highlight the challenges of achieving representation disentanglement for text domain in an unsupervised setting, in this paper we select a representative set of successfully applied models from the image domain. We evaluate these models on 6 disentanglement metrics, as well as on downstream classification tasks and homotopy. To facilitate the evaluation, we propose two synthetic datasets with known generative factors. Our experiments highlight the existing gap in the text domain and illustrate that certain elements such as representation sparsity (as an inductive bias), or representation coupling with the decoder could impact disentanglement. To the best of our knowledge, our work is the first attempt on the intersection of unsupervised representation disentanglement and text, and provides the experimental framework and datasets for examining future developments in this direction.
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