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محور في مهمة Semeval-2021 1: الانصهار من الجملة وتردد الكلمات للتنبؤ التعقيد المعجمي
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في هذه الورقة، نقترح طريقة لاستدادتها معلومات جملة المعلومات ومعلومات تردد الكلمات الخاصة بمهمة التعقيد ذات التعقيد 1-LCP (LCP). في نظامنا، تأتي معلومات الجملة من نموذج روبرتا، وتأتي معلومات تردد الكلمات من خوارزمية TF-IDF. استخدم Black Block كطبقة مشتركة لتعلم العقوبة ومعلومات تردد الكلمات وصفنا تنفيذ أفضل نظامنا وناقش أساليبنا وتجاربنا في المهمة. تنقسم المهمة المشتركة إلى مهمتين فرعيتين. الهدف من المهام الفرعية هو التنبؤ بعقد كلمة محددة سلفا. تنقسم المهمة المشتركة إلى قسمين فرعيين. الهدف من اثنين من المهن الفرعية هو التنبؤ بعقد كلمة محددة سلفا. مؤشر تقييم المهمة هو معامل الارتباط بيرسون. يحتوي أفضل نظام الأداء لدينا على معاملات ارتباط بيرسون من 0.7434 و 0.8000 في مجموعة اختبار المراكز الفرعية ذات الرمز الفرعي واحد ومجموعة اختبار الترجمة الفرعية متعددة رميات، على التوالي.
In this paper, we propose a method of fusing sentence information and word frequency information for the SemEval 2021 Task 1-Lexical Complexity Prediction (LCP) shared task. In our system, the sentence information comes from the RoBERTa model, and the 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.
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Lexical complexity prediction (LCP) conveys the anticipation of the complexity level of a token or a set of tokens in a sentence. It plays a vital role in the improvement of various NLP tasks including lexical simplification, translations, and text g
eneration. However, multiple meaning of a word in multiple circumstances, grammatical complex structure, and the mutual dependency of words in a sentence make it difficult to estimate the lexical complexity. To address these challenges, SemEval-2021 Task 1 introduced a shared task focusing on LCP and this paper presents our participation in this task. We proposed a transformer-based approach with sentence pair regression. We employed two fine-tuned transformer models. Including BERT and RoBERTa to train our model and fuse their predicted score to the complexity estimation. Experimental results demonstrate that our proposed method achieved competitive performance compared to the participants' systems.
This paper revisits feature engineering approaches for predicting the complexity level of English words in a particular context using regression techniques. Our best submission to the Lexical Complexity Prediction (LCP) shared task was ranked 3rd out
of 48 systems for sub-task 1 and achieved Pearson correlation coefficients of 0.779 and 0.809 for single words and multi-word expressions respectively. The conclusion is that a combination of lexical, contextual and semantic features can still produce strong baselines when compared against human judgement.
Evaluating the complexity of a target word in a sentential context is the aim of the Lexical Complexity Prediction task at SemEval-2021. This paper presents the system created to assess single words lexical complexity, combining linguistic and psycho
linguistic variables in a set of experiments involving random forest and XGboost regressors. Beyond encoding out-of-context information about the lemma, we implemented features based on pre-trained language models to model the target word's in-context complexity.
This paper presents the results and main findings of SemEval-2021 Task 1 - Lexical Complexity Prediction. We provided participants with an augmented version of the CompLex Corpus (Shardlow et al. 2020). CompLex is an English multi-domain corpus in wh
ich words and multi-word expressions (MWEs) were annotated with respect to their complexity using a five point Likert scale. SemEval-2021 Task 1 featured two Sub-tasks: Sub-task 1 focused on single words and Sub-task 2 focused on MWEs. The competition attracted 198 teams in total, of which 54 teams submitted official runs on the test data to Sub-task 1 and 37 to Sub-task 2.
This paper describes our contribution to SemEval 2021 Task 1 (Shardlow et al., 2021): Lexical Complexity Prediction. In our approach, we leverage the ELECTRA model and attempt to mirror the data annotation scheme. Although the task is a regression ta
sk, we show that we can treat it as an aggregation of several classification and regression models. This somewhat counter-intuitive approach achieved an MAE score of 0.0654 for Sub-Task 1 and MAE of 0.0811 on Sub-Task 2. Additionally, we used the concept of weak supervision signals from Gloss-BERT in our work, and it significantly improved the MAE score in Sub-Task 1.
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