تصف هذه الورقة التقديم الخاص بنا إلى مهمة Semeval 2021 2. نحن نقارن قاعدة XLM-Roberta وكبير في إعدادات القليل من اللقطات والطلق الرصاص واختبار فعاليا فعالية استخدام مصنف جيران K-Enter في إعداد القليل من القصاصات بدلا منأكثر التقليدية متعددة الطبقات perceptron.تظهر تجاربنا على كل من البيانات متعددة اللغات واللغة أن XLM-Roberta Large، على عكس الإصدار الأساسي، يمكن أن يكون قادرا على نقل التعلم بشكل أكثر فعالية في بيض بضع طلقة وأن مصنف الجيران K-Neave هو في الواقعمصنف أكثر قوة من بيرسيبترون متعدد الطبقات عند استخدامه في التعلم القليل من اللقطة.
This paper describes our submission to SemEval 2021 Task 2. We compare XLM-RoBERTa Base and Large in the few-shot and zero-shot settings and additionally test the effectiveness of using a k-nearest neighbors classifier in the few-shot setting instead of the more traditional multi-layered perceptron. Our experiments on both the multi-lingual and cross-lingual data show that XLM-RoBERTa Large, unlike the Base version, seems to be able to more effectively transfer learning in a few-shot setting and that the k-nearest neighbors classifier is indeed a more powerful classifier than a multi-layered perceptron when used in few-shot learning.
References used
https://aclanthology.org/
Identifying whether a word carries the same meaning or different meaning in two contexts is an important research area in natural language processing which plays a significant role in many applications such as question answering, document summarisati
In this paper, we introduce our system that we participated with at the multilingual and cross-lingual word-in-context disambiguation SemEval 2021 shared task. In our experiments, we investigated the possibility of using an all-words fine-grained wor
In this paper, we introduce the first SemEval task on Multilingual and Cross-Lingual Word-in-Context disambiguation (MCL-WiC). This task allows the largely under-investigated inherent ability of systems to discriminate between word senses within and
This paper presents a word-in-context disambiguation system. The task focuses on capturing the polysemous nature of words in a multilingual and cross-lingual setting, without considering a strict inventory of word meanings. The system applies Natural
We experiment with XLM RoBERTa for Word in Context Disambiguation in the Multi Lingual and Cross Lingual setting so as to develop a single model having knowledge about both settings. We solve the problem as a binary classification problem and also ex