Multilingual Lexical Complexity Prediction and Lexical Simplification Resources for Catalan and Spanish

Core Concepts

This paper presents MultiLS-SP/CA, a novel dataset for lexical simplification in Spanish and Catalan, which includes scalar ratings of the understanding difficulty of lexical items. The dataset represents the first of its kind in Catalan and a substantial addition to the sparse data on automatic lexical simplification available for Spanish.

Abstract

The paper presents two new datasets, MultiLS-SP and MultiLS-CA, for lexical simplification in Spanish and Catalan, respectively. The key highlights are: MultiLS-SP/CA is the first dataset for lexical simplification in Catalan and a significant addition to the limited data available for Spanish. The datasets include not only complex words that can be simplified, but also non-substitutable words, providing a more comprehensive scenario for system development. Each target word is annotated with a scalar rating of lexical complexity on a 5-point Likert scale, in addition to up to 3 lexical substitutes. The paper describes the data compilation process and provides baseline performance for lexical simplification and lexical complexity prediction tasks using the new datasets. The baseline results indicate substantial room for improvement, highlighting the need for more advanced models and the value of these new resources for the Iberian Romance languages.

Stats

The datasets contain a total of 1,105 target words (625 in Spanish, 480 in Catalan) embedded in 370 context sentences (210 in Spanish, 160 in Catalan).

Quotes

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Key Insights Distilled From

MultiLS-SP/CA

by Stef... at arxiv.org 04-12-2024

https://arxiv.org/pdf/2404.07814.pdf

Deeper Inquiries

How can the lexical complexity prediction models be improved beyond the simple regression baseline presented in the paper?

To improve lexical complexity prediction models beyond the simple regression baseline, more advanced machine learning techniques can be employed. One approach is to utilize deep learning models such as recurrent neural networks (RNNs), long short-term memory (LSTM) networks, or transformer models like BERT. These models can capture complex patterns in the data and learn hierarchical representations of words and phrases, which can lead to more accurate predictions of lexical complexity. Additionally, incorporating linguistic features such as part-of-speech tags, syntactic structures, semantic information, and contextual embeddings can enhance the model's predictive capabilities. By combining these advanced techniques and linguistic features, the model can better capture the nuances of lexical complexity in the languages under consideration.

How can the lexical simplification systems be extended to handle not only single-word substitutions, but also multi-word expressions and more complex linguistic transformations?

To extend lexical simplification systems to handle multi-word expressions and more complex linguistic transformations, a few strategies can be implemented. One approach is to incorporate phrase-based or sentence-level simplification techniques that consider the context of the words and phrases within the larger linguistic context. This can involve using syntactic parsing to identify phrases or chunks of text that can be simplified together. Additionally, leveraging pre-trained language models like GPT-3 or T5 that are capable of generating coherent and contextually relevant text can aid in handling multi-word expressions and complex transformations. These models can generate paraphrases or simplified versions of entire sentences or paragraphs, ensuring that the meaning and coherence of the text are preserved during simplification.

What other linguistic features, beyond word frequency and length, could be leveraged to better predict lexical complexity in these languages?

In addition to word frequency and length, several other linguistic features can be leveraged to better predict lexical complexity in these languages. Some of these features include: Morphological complexity: Considering the morphological structure of words, such as prefixes, suffixes, and inflections, can provide insights into the complexity of a word. Semantic ambiguity: Words with multiple meanings or ambiguous interpretations can contribute to lexical complexity. Syntactic complexity: Analyzing the syntactic structures in which words appear can indicate their level of complexity. Concreteness and abstractness: Words that represent concrete concepts are generally easier to understand than abstract or technical terms. Domain-specificity: Words from specialized domains or technical fields may be more complex due to their specific meanings and usage. By incorporating these linguistic features into the lexical complexity prediction models, a more comprehensive understanding of lexical complexity can be achieved, leading to more accurate predictions and better performance of the models.