Articles

AbstractThis paper presents the PALI team’s winning system for SemEval-2021 Task 2: Multilingual and Cross-lingual Word-in-Context Disambiguation. We fine-tune XLM-RoBERTa model to solve the task of word in context disambiguation, i.e., to determine whether the target word in the two contexts contains the same meaning or not. In implementation, we …...

AbstractThis paper presents the GX system for the Multilingual and Cross-lingual Word-in-Context Disambiguation (MCL-WiC) task. The purpose of the MCL-WiC task is to tackle the challenge of capturing the polysemous nature of words without relying on a fixed sense inventory in a multilingual and cross-lingual setting. To solve the problems, …...

AbstractThis paper describes systems submitted to Se- mEval 2021 Task 1: Lexical Complexity Prediction (LCP). We compare a linear and a non-linear regression models trained to work for both tracks of the task. We show that both systems are able to generalize better when supplied with information about complexities of …...

AbstractEvaluating 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 psycholinguistic variables in a set of experiments involving random forest and XGboost regressors. …...

AbstractThis paper presents our system for the single- and multi-word lexical complexity prediction tasks of SemEval Task 1: Lexical Complexity Prediction. Text comprehension depends on the reader’s ability to understand the words present in it; evaluating the lexical complexity of such texts can enable readers to find an appropriate text …...

AbstractThe main contribution of this paper is to fine-tune transformer-based language models pre-trained on several text corpora, some being general (E.g., Wikipedia, BooksCorpus), some being the corpora from which the CompLex Dataset was extracted, and others being from other specific domains such as Finance, Law, etc. We perform ablation studies …...

AbstractWe present our approach to predicting lexical complexity of words in specific contexts, as entered LCP Shared Task 1 at SemEval 2021. The approach consists of separating sentences into smaller chunks, embedding them with Sent2Vec, and reducing the embeddings into a simpler vector used as input to a neural network, …...

AbstractThis 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 …...

AbstractPredicting the complexity level of a word or a phrase is considered a challenging task. It is even recognized as a crucial step in numerous NLP applications, such as text rearrangements and text simplification. Early research treated the task as a binary classification task, where the systems anticipated the existence …...

AbstractIn this paper, we present three supervised systems for English lexical complexity prediction of single and multiword expressions for SemEval-2021 Task 1. We explore the use of statistical baseline features, masked language models, and character-level encoders to predict the complexity of a target token in context. Our best system combines …...

AbstractWe present the technical report of the system called RS_GV at SemEval-2021 Task 1 on lexical complexity prediction of English words. RS_GV is a neural network using hand-crafted linguistic features in combination with character and word embeddings to predict target words’ complexity. For the generation of the hand-crafted features, we …...

AbstractThis paper presents the system we submitted to the first Lexical Complexity Prediction (LCP) Shared Task 2021. The Shared Task provides participants with a new English dataset that includes context of the target word. We participate in the single-word complexity prediction sub-task and focus on feature engineering. Our best system …...

AbstractLexical 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 generation. However, multiple meaning of a word in multiple circumstances, …...

AbstractIn this paper we propose a contextual attention based model with two-stage fine-tune training using RoBERTa. First, we perform the first-stage fine-tune on corpus with RoBERTa, so that the model can learn some prior domain knowledge. Then we get the contextual embedding of context words based on the token-level embedding …...

AbstractIn this paper we describe our participation in the Lexical Complexity Prediction (LCP) shared task of SemEval 2021, which involved predicting subjective ratings of complexity for English single words and multi-word expressions, presented in context. Our approach relies on a combination of distributional models, both context-dependent and context-independent, together with …...

AbstractReading is a complex process which requires proper understanding of texts in order to create coherent mental representations. However, comprehension problems may arise due to hard-to-understand sections, which can prove troublesome for readers, while accounting for their specific language skills. As such, steps towards simplifying these sections can be performed, …...

AbstractWe describe the UTFPR systems submitted to the Lexical Complexity Prediction shared task of SemEval 2021. They perform complexity prediction by combining classic features, such as word frequency, n-gram frequency, word length, and number of senses, with BERT vectors. We test numerous feature combinations and machine learning models in our …...

AbstractWe present two convolutional neural networks for predicting the complexity of words and phrases in context on a continuous scale. Both models utilize word and character embeddings alongside lexical features as inputs. Our system displays reasonable results with a Pearson correlation of 0.7754 on the task as a whole. We …...

AbstractIn 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 …...

AbstractLexical 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 …...