SemEval-2021 Task 1: Lexical Complexity Prediction

Shardlow, Matthew; Evans, Richard J.; Paetzold, Gustavo Henrique; Zampieri, Marcos

doi:10.18653/v1/2021.semeval-1.1

Cited by 54 publications

(64 citation statements)

References 53 publications

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“…We consider as baselines two models used for the LCP 2021 competition (Shardlow et al, 2021a), as well as the best-registered score. Almeida et al (2021) employed the usage of neural network solutions; more specifically, they used chunks of the sentences obtained with Sent2Vec as input features.…”

Section: Lcp 2021 Complex Datasetmentioning

confidence: 99%

Domain Adaptation in Multilingual and Multi-Domain Monolingual Settings for Complex Word Identification

Zaharia¹,

Smădu²,

Cercel³

et al. 2022

Preprint

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Complex word identification (CWI) is a cornerstone process towards proper text simplification. CWI is highly dependent on context, whereas its difficulty is augmented by the scarcity of available datasets which vary greatly in terms of domains and languages. As such, it becomes increasingly more difficult to develop a robust model that generalizes across a wide array of input examples. In this paper, we propose a novel training technique for the CWI task based on domain adaptation to improve the target character and context representations. This technique addresses the problem of working with multiple domains, inasmuch as it creates a way of smoothing the differences between the explored datasets. Moreover, we also propose a similar auxiliary task, namely text simplification, that can be used to complement lexical complexity prediction. Our model obtains a boost of up to 2.42% in terms of Pearson Correlation Coefficients in contrast to vanilla training techniques, when considering the CompLex from the Lexical Complexity Prediction 2021 dataset. At the same time, we obtain an increase of 3% in Pearson scores, while considering a cross-lingual setup relying on the Complex Word Identification 2018 dataset. In addition, our model yields state-ofthe-art results in terms of Mean Absolute Error.

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Section: Lcp 2021 Complex Datasetmentioning

confidence: 99%

Domain Adaptation in Multilingual and Multi-Domain Monolingual Settings for Complex Word Identification

Zaharia¹,

Smădu²,

Cercel³

et al. 2022

Preprint

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“…Up to this point, tasks as CWI are treated as a binary problem where the goal is to determine whether a word is simple or complex. However, a recent workshop [136] at SemEval 2021, changes this premise by assigning a continuous scale value to identify the complexity of a word, which they call Lexical Complexity Prediction (LCP), which at the end is very related to both CWI and SR steps. The task offered two sub-tasks, one for uniwords and one for uniwords/multiwords.…”

Section: Substitute Ranking (Sr)mentioning

confidence: 99%

Designing and Evaluating a User Interface for People with Cognitive Disabilities

Moreno

Alarcón

Martı́nez

2021

Proceedings of the XXI International Conference on Human Computer Interaction

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The Internet has come a long way in recent years, contributing to the proliferation of large volumes of digitally available information. Through user interfaces we can access these contents, however, they are not accessible to everyone. The main users affected are people with disabilities, who are already a considerable number, but accessibility barriers affect a wide range of user groups and contexts of use in accessing digital information. Some of these barriers are caused by language inaccessibility when texts contain long sentences, unusual words and complex linguistic structures. These accessibility barriers directly affect people with cognitive disabilities. ResumenInternet ha avanzado mucho en los últimos años contribuyendo a la proliferación de grandes volúmenes de información disponible digitalmente. A través de interfaces de usuario podemos acceder a estos contenidos, sin embargo, estos no son accesibles a todas las personas. Los usuarios afectados principalmente son las personas con discapacidad siendo ya un número considerable, pero las barreras de accesibilidad afectan a un gran rango de grupos de usuarios y contextos de uso en el acceso a la información digital. Algunas de estas barreras son causadas por la inaccesibilidad al lenguaje cuando los textos contienen oraciones largas, palabras inusuales y estructuras lingüísticas complejas. Estas barreras de accesibilidad afectan directamente a las personas con discapacidad cognitiva.Con el fin de hacer el contenido textual más accesible, existen iniciativas como las pautas de Lectura Fácil, las pautas de Lenguaje Claro y algunas de las pautas de Accesibilidad al Contenido en la Web (WCAG) específicas para el lenguaje. Estas pautas proporcionan documentación, pero no especifican métodos para cumplir con los requisitos implícitos en estas pautas de manera sistemática. Para obtener una solución, los métodos de la disciplina del Procesamiento del Lenguaje Natural (PLN) pueden dar un soporte para alcanzar la conformidad con las pautas de accesibilidad cognitiva relativas al lenguaje La tarea de la simplificación de textos del PLN tiene como objetivo reducir la complejidad lingüística de un texto desde una perspectiva sintáctica y léxica, siendo esta última el enfoque principal de esta Tesis. En este sentido, un espacio de solución es identificar en un texto qué palabras son complejas o poco comunes, y en el caso de que sí hubiera, proporcionar un sinónimo más usual y sencillo, junto con una definición sencilla, todo ello orientado a las personas con discapacidad cognitiva.Con tal meta, en esta Tesis, se presenta el estudio, análisis, diseño y desarrollo de una arquitectura, métodos PLN, recursos y herramientas para la simplificación léxica de textos para el idioma español en un dominio genérico en el ámbito de la accesibilidad cognitiva. Para lograr esto, se estudia cada uno de los pasos presentes en los procesos de simplificación léxica, junto con métodos para la desambiguación del sentido de las palabras. Como contribución, diferentes tipos de word embeddi...

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“…Our task of lexical complexity prediction is an important step in the process of simplifying texts. The SemEval 2021 Task 1 (Shardlow et al, 2021) focuses on lexical complexity prediction in English. Given a sentence and a token from it, we have to predict the complexity score of the token.…”

Section: Introductionmentioning

confidence: 99%

IITK@LCP at SemEval-2021 Task 1: Classification for Lexical Complexity Regression Task

Shirude¹,

Mukherjee²,

Shandhilya³

et al. 2021

Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)

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This paper describes our contribution to Se-mEval 2021 Task 1: 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 task, we show that we can treat it as an aggregation of several classification and regression models. This somewhat counterintuitive 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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SemEval-2021 Task 1: Lexical Complexity Prediction

Cited by 54 publications

References 53 publications

Domain Adaptation in Multilingual and Multi-Domain Monolingual Settings for Complex Word Identification

Domain Adaptation in Multilingual and Multi-Domain Monolingual Settings for Complex Word Identification

Designing and Evaluating a User Interface for People with Cognitive Disabilities

IITK@LCP at SemEval-2021 Task 1: Classification for Lexical Complexity Regression Task

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