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Register a new userBenchmarking Meta-embeddings: What Works and What Does Not
معيار ميتا - Embeddings: ما الذي يعمل وما لا
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في السنوات القليلة الماضية، تم اقتراح العديد من الطرق لبناء تضمين التوطين.كان الهدف العام هو الحصول على تمثيلات جديدة تدمج المعرفة التكميلية من مختلف المدينات المدربة مسبقا مما يؤدي إلى تحسين الجودة الشاملة.ومع ذلك، تم تقييم Enterpaintings Meta-embeddings السابق باستخدام مجموعة متنوعة من الأساليب ومجموعات البيانات، مما يجعل من الصعب استخلاص استنتاجات ذات مغزى بشأن مزايا كل منهج.في هذه الورقة نقترح إطارا مشتركا موحدا، بما في ذلك المهام الجوهرية والخارجية، من أجل تقييم عادل وموضوعي لتقييم التوطين.علاوة على ذلك، نقدم طريقة جديدة لتوليد تضمين التوطين، مما يفوقن العمل السابق على عدد كبير من معايير التقييم الجوهرية.كما يتيح لنا إطار التقييم أن نستنتج أن التقييمات الخارجية السابقة للمضفة المتمثلة في المبالغة في تقديرها.
In the last few years, several methods have been proposed to build meta-embeddings. The general aim was to obtain new representations integrating complementary knowledge from different source pre-trained embeddings thereby improving their overall quality. However, previous meta-embeddings have been evaluated using a variety of methods and datasets, which makes it difficult to draw meaningful conclusions regarding the merits of each approach. In this paper we propose a unified common framework, including both intrinsic and extrinsic tasks, for a fair and objective meta-embeddings evaluation. Furthermore, we present a new method to generate meta-embeddings, outperforming previous work on a large number of intrinsic evaluation benchmarks. Our evaluation framework also allows us to conclude that previous extrinsic evaluations of meta-embeddings have been overestimated.
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