Multi-Word Lexical Simplification

Przybyła, Piotr; Shardlow, Matthew

doi:10.18653/v1/2020.coling-main.123

Cited by 6 publications

(5 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Currently, the best performing LS system for English is the LSBert system (Qiang et al, 2020a ), which uses pre-trained transformer language model BERT (Devlin et al, 2019 ) and a masking technique for finding suitable simplifications for complex words. This approach was further extended by Przybyła and Shardlow ( 2020 ) to build a multi-word LS system for English. The LSBert system (Qiang et al, 2020a ) and our adaptation of it to Spanish and Portuguese will be described in more details in Section 4.2.…”

Section: Related Workmentioning

confidence: 99%

Lexical simplification benchmarks for English, Portuguese, and Spanish

Štajner¹,

Ferrés²,

Shardlow³

et al. 2022

Front. Artif. Intell.

Self Cite

View full text Add to dashboard Cite

Even in highly-developed countries, as many as 15–30% of the population can only understand texts written using a basic vocabulary. Their understanding of everyday texts is limited, which prevents them from taking an active role in society and making informed decisions regarding healthcare, legal representation, or democratic choice. Lexical simplification is a natural language processing task that aims to make text understandable to everyone by replacing complex vocabulary and expressions with simpler ones, while preserving the original meaning. It has attracted considerable attention in the last 20 years, and fully automatic lexical simplification systems have been proposed for various languages. The main obstacle for the progress of the field is the absence of high-quality datasets for building and evaluating lexical simplification systems. In this study, we present a new benchmark dataset for lexical simplification in English, Spanish, and (Brazilian) Portuguese, and provide details about data selection and annotation procedures, to enable compilation of comparable datasets in other languages and domains. As the first multilingual lexical simplification dataset, where instances in all three languages were selected and annotated using comparable procedures, this is the first dataset that offers a direct comparison of lexical simplification systems for three languages. To showcase the usability of the dataset, we adapt two state-of-the-art lexical simplification systems with differing architectures (neural vs. non-neural) to all three languages (English, Spanish, and Brazilian Portuguese) and evaluate their performances on our new dataset. For a fairer comparison, we use several evaluation measures which capture varied aspects of the systems' efficacy, and discuss their strengths and weaknesses. We find that a state-of-the-art neural lexical simplification system outperforms a state-of-the-art non-neural lexical simplification system in all three languages, according to all evaluation measures. More importantly, we find that the state-of-the-art neural lexical simplification systems perform significantly better for English than for Spanish and Portuguese, thus posing a question if such an architecture can be used for successful lexical simplification in other languages, especially the low-resourced ones.

show abstract

Section: Related Workmentioning

confidence: 99%

Lexical simplification benchmarks for English, Portuguese, and Spanish

Štajner¹,

Ferrés²,

Shardlow³

et al. 2022

Front. Artif. Intell.

Self Cite

View full text Add to dashboard Cite

show abstract

“…frequency, vectorbased semantic similarity, and/or language model probability. Studies using LSBert (Przybyła and Shardlow, 2020;Štajner et al, 2022) have shown that the approach could easily be adapted to other languages and still achieve state-of-the-art results.…”

Section: Related Workmentioning

confidence: 99%

PolyU-CBS at TSAR-2022 Shared Task: A Simple, Rank-Based Method for Complex Word Substitution in Two Steps

Chersoni,

Hsu

2022

Proceedings of the Workshop on Text Simplification, Accessibility, and Readability (TSAR-2022)

View full text Add to dashboard Cite

In this paper, we describe the system we present at the Workshop on Text Simplification, Accessibility, and Readability (TSAR-2022) regarding the shared task on Lexical Simplification for English, Portuguese, and Spanish. We proposed an unsupervised approach in two steps: First, we used a masked language model with word masking for each language to extract possible candidates for the replacement of a difficult word; second, we ranked the candidates according to three different Transformer-based metrics. Finally, we determined our list of candidates based on the lowest average rank across different metrics. The results show that our method, based on two simple steps and rankings, can effectively improve the scores among datasets for the task of lexical simplification.

show abstract

“…Recently, simplification systems have focused on the use of transformer architecture to identify appropriate replacements for a given word (Qiang et al, 2021). This can be applied at a single or multi-word level (Przybyła and Shardlow, 2020).…”

Section: Related Workmentioning

confidence: 99%

UoM&MMU at TSAR-2022 Shared Task: Prompt Learning for Lexical Simplification

Vásquez-Rodríguez,

Nguyen,

Shardlow

et al. 2022

Proceedings of the Workshop on Text Simplification, Accessibility, and Readability (TSAR-2022)

View full text Add to dashboard Cite

We present PromptLS, a method for fine-tuning large pre-trained Language Models (LM) to perform the task of Lexical Simplification. We use a predefined template to attain appropriate replacements for a term, and fine-tune a LM using this template on language specific datasets. We filter candidate lists in post-processing to improve accuracy. We demonstrate that our model can work in a) a zero shot setting (where we only require a pre-trained LM), b) a fine-tuned setting (where language-specific data is required), and c) a multilingual setting (where the model is pre-trained across multiple languages and fine-tuned in an specific language). Experimental results show that, although the zero-shot setting is competitive, its performance is still far from the fine-tuned setting. Also, the multilingual is unsurprisingly worse than the finetuned model. Among all TSAR-2022 Shared Task participants, our team was ranked second in Spanish and third in English.

show abstract

Multi-Word Lexical Simplification

Cited by 6 publications

References 23 publications

Lexical simplification benchmarks for English, Portuguese, and Spanish

Lexical simplification benchmarks for English, Portuguese, and Spanish

PolyU-CBS at TSAR-2022 Shared Task: A Simple, Rank-Based Method for Complex Word Substitution in Two Steps

UoM&MMU at TSAR-2022 Shared Task: Prompt Learning for Lexical Simplification

Contact Info

Product

Resources

About