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HUB في Semeval-2021 المهمة 2: كلمة معنى تنبؤ التشابه بناء على روبرتا وتردد الكلمات
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تقدم هذه الورقة وصف نظام فريق المحور، الذي يفسر العمل ذي الصلة والنتائج التجريبية لمشاركة فريقنا في مهمة Semeval 2021: الغموض المتعدد اللغات والتبلغة في السياق (MCL-WIC). بيانات هذه المهمة المشتركة هي أساسا بعض الزوجات الزوجية عبر اللغة أو متعددة اللغات. اللغات المشمولة في Corpus تشمل اللغة الإنجليزية والصينية والفرنسية والروسية والعربية. الهدف المهمة هو الحكم على ما إذا كانت الكلمات نفسها في أزواج هذه الجملة لها نفس المعنى في الجملة. يمكن اعتبار ذلك مهمة التصنيف الثنائي لأزواج الجملة. ما نحتاج إليه هو استخدام طريقتنا لتحديد بدقة قدر الإمكان معنى الكلمات في زوج الجملة هي نفسها أو مختلفة. يتكون النموذج المستخدم من قبل فريقنا بشكل أساسي من خوارزميات روبرتا و TF-IDF. مؤشر تقييم النتائج لتقديم المهمة هو درجة F1. شاركنا فقط في مهمة اللغة الإنجليزية. وكانت النتيجة النهائية لنتائج التنبؤ بمجموعة الاختبار المقدمة من فريقنا 84.60.
This paper introduces the system description of the hub team, which explains the related work and experimental results of our team's participation in SemEval 2021 Task 2: Multilingual and Cross-lingual Word-in-Context Disambiguation (MCL-WiC). The data of this shared task is mainly some cross-language or multi-language sentence pair corpus. The languages covered in the corpus include English, Chinese, French, Russian, and Arabic. The task goal is to judge whether the same words in these sentence pairs have the same meaning in the sentence. This can be seen as a task of binary classification of sentence pairs. What we need to do is to use our method to determine as accurately as possible the meaning of the words in a sentence pair are the same or different. The model used by our team is mainly composed of RoBERTa and Tf-Idf algorithms. The result evaluation index of task submission is the F1 score. We only participated in the English language task. The final score of the test set prediction results submitted by our team was 84.60.
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This article introduces the system description of the hub team, which explains the related work and experimental results of our team's participation in SemEval 2021 Task 5: Toxic Spans Detection. The data for this shared task comes from some posts on
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This paper introduces the system description of the hub team, which explains the related work and experimental results of our team's participation in SemEval 2021 Task 7: HaHackathon: Detecting and Rating Humor and Offense. We successfully submitted
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e word frequency information comes from the Tf-Idf algorithm. Use Inception block as a shared layer to learn sentence and word frequency information We described the implementation of our best system and discussed our methods and experiments in the task. The shared task is divided into two sub-tasks. The goal of the two sub-tasks is to predict the complexity of a predetermined word. The shared task is divided into two subtasks. The goal of the two subtasks is to predict the complexity of a predetermined word. The evaluation index of the task is the Pearson correlation coefficient. Our best performance system has Pearson correlation coefficients of 0.7434 and 0.8000 in the single-token subtask test set and the multi-token subtask test set, respectively.
In this paper, we describe our proposed methods for the multilingual word-in-Context disambiguation task in SemEval-2021. In this task, systems should determine whether a word that occurs in two different sentences is used with the same meaning or no
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