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Register a new userBigGreen at SemEval-2021 Task 1: Lexical Complexity Prediction with Assembly Models
Biggreen في مهمة Semeval-2021 1: تنبؤ التعقيد المعجمي مع نماذج التجميع
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تصف هذه الورقة نظام مقدم من فريق Biggreen إلى LCP 2021 للتنبؤ بالتعقيد المعجمي للكلمات الإنجليزية في سياق معين.نحن نكرب نموذجا يعتمد على الهندسة مع نموذج شبكة عصبي عميق تأسست على بيرتف.بينما ينفذ بيرت نفسها بشكل تنافسي، فإن نموذجنا القائم على الهندسة يساعد في الحالات القصوى، على سبيل المثال.فصل حالات الصعوبة السهلة والمحايدة.تضم ميزاتنا المصنوعة يدويا اتساعا من التدابير الصوفية المعجمية والدلية والمعنية والرواية.تقدم تصورات خرائط بيرت اهتماما نظرة ثاقبة للميزات المحتملة التي قد تتعلمها نماذج المحولات عند ضبطها من أجل تنبؤ التعقيد المعجمي.تنقيح تنبؤاتنا المعقولة بشكل معقول بالنسبة للكلمة الفرعية الواحدة، ونظهر كيف يمكن تسخيرها لأداء الاستاحا الفرعي للتعبير المتعدد الآن.
This paper describes a system submitted by team BigGreen to LCP 2021 for predicting the lexical complexity of English words in a given context. We assemble a feature engineering-based model with a deep neural network model founded on BERT. While BERT itself performs competitively, our feature engineering-based model helps in extreme cases, eg. separating instances of easy and neutral difficulty. Our handcrafted features comprise a breadth of lexical, semantic, syntactic, and novel phonological measures. Visualizations of BERT attention maps offer insight into potential features that Transformers models may learn when fine-tuned for lexical complexity prediction. Our ensembled predictions score reasonably well for the single word subtask, and we demonstrate how they can be harnessed to perform well on the multi word expression subtask too.
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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.
Lexical complexity plays an important role in reading comprehension. lexical complexity prediction (LCP) can not only be used as a part of Lexical Simplification systems, but also as a stand-alone application to help people better reading. This paper
presents the winning system we submitted to the LCP Shared Task of SemEval 2021 that capable of dealing with both two subtasks. We first perform fine-tuning on numbers of pre-trained language models (PLMs) with various hyperparameters and different training strategies such as pseudo-labelling and data augmentation. Then an effective stacking mechanism is applied on top of the fine-tuned PLMs to obtain the final prediction. Experimental results on the Complex dataset show the validity of our method and we rank first and second for subtask 2 and 1.
In this contribution, we describe the system presented by the PolyU CBS-Comp Team at the Task 1 of SemEval 2021, where the goal was the estimation of the complexity of words in a given sentence context. Our top system, based on a combination of lexic
al, syntactic, word embeddings and Transformers-derived features and on a Gradient Boosting Regressor, achieves a top correlation score of 0.754 on the subtask 1 for single words and 0.659 on the subtask 2 for multiword expressions.
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.
In this paper, we present our contribution in SemEval-2021 Task 1: Lexical Complexity Prediction, where we integrate linguistic, statistical, and semantic properties of the target word and its context as features within a Machine Learning (ML) framew
ork for predicting lexical complexity. In particular, we use BERT contextualized word embeddings to represent the semantic meaning of the target word and its context. We participated in the sub-task of predicting the complexity score of single words
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