United Arab Emirates

Text simplification is a task to reduce the complexity of a text while retain its original meaning. It can facilitate people with low-literacy skills or language impairments, such as children and individuals with dyslexia and aphasia, to read and understand complicated materials. Normally, substitution, deletion, reordering, and splitting are considered as four core operations for performing text simplification. Thus an ideal model should be capable of executing these operations appropriately to simplify a text. However, by examining the degree that each operation is exerted in different datasets, we observe that there is a salient discrepancy between the human annotation and existing training data that is widely used for training simplification models. To alleviate this discrepancy, we propose an unsupervised data construction method that distills each simplifying operation into data via different automatic data enhancement measures. The empirical results demonstrate that the resulting dataset SimSim can support models to achieve better performance by performing all operations properly.

EMNLP 2022

Divide-and-Conquer Text Simplification by Scalable Data Enhancement

data construction

sentence simplification

natural language processing

workshop paper

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In the past few years, the field of text simplification has been dominated by supervised learning approaches thanks to the appearance of large parallel datasets such as Wikilarge and Newsela. However, these datasets suffer from sentence pairs with factuality errors which compromise the models' performance. So, we proposed a model-independent factuality error detection mechanism, considering bad simplification and bad alignment, to refine the Wikilarge dataset through reducing the weight of these samples during training. We demonstrated that this approach improved the performance of the state-of-the-art text simplification model TST5 by an FKGL reduction of 0.33 and 0.29 on the TurkCorpus and ASSET testing datasets respectively. Our study illustrates the impact of erroneous samples in TS datasets and highlights the need for automatic methods to improve their quality.

Improving Text Simplification with Factuality Error Detection

The user-dependency of Text Simplification makes its evaluation obscure. A targeted evaluation dataset clarifies the purpose of simplification, though its specification is hard to define. We built JADES (JApanese Dataset for the Evaluation of Simplification), a text simplification dataset targeted at non-native Japanese speakers, according to public vocabulary and grammar profiles. JADES comprises 3,907 complex-simple sentence pairs annotated by an expert. Analysis of JADES shows that wide and multiple rewriting operations were applied through simplification. Furthermore, we analyzed  outputs on JADES from several benchmark systems and automatic and manual scores of them. Results of these analyses highlight differences between English and Japanese in operations and evaluations.

JADES: New Text Simplification Dataset in Japanese Targeted at Non-Native Speakers

We release a new benchmark for Automated Readability Assessment (ARA) of texts in Spanish. We combined existing corpora with suitable texts collected from the Web, thus creating the largest available dataset for ARA of Spanish texts. All data was pre-processed and categorised to allow experimenting with ARA models that make predictions at two (simple and complex) or three (basic, intermediate, and advanced) readability levels, and at two text granularities (paragraphs and sentences). An analysis based on readability indices shows that our proposed datasets groupings are suitable for their designated readability level. We use our benchmark to train neural ARA models based on BERT in zero-shot, few-shot, and cross-lingual settings. Results show that either a monolingual or multilingual pre-trained model can achieve good results when fine-tuned in language-specific data. In addition, all mod- els decrease their performance when predicting three classes instead of two, showing opportunities for the development of better ARA models for Spanish with existing resources.

A Benchmark for Neural Readability Assessment of Texts in Spanish

Fine-tuning Transformer-based approaches have recently shown exciting results on sentence simplification task. However, so far, no research has applied similar approaches to the Lexical Simplification (LS) task. In this paper, we present ConLS, a Controllable Lexical Simplification system fine-tuned with T5 (a Transformer-based model pre-trained with a BERT-style approach and several other tasks). The evaluation results on three datasets (LexMTurk, BenchLS, and NNSeval) have shown that our model performs comparable to LSBert (the current state-of-the-art) and even outperforms it in some cases. We also conducted a detailed comparison on the effectiveness of control tokens to give a clear view of how each token contributes to the model.

Controllable Lexical Simplification for English

We report findings of the TSAR-2022 shared task on multilingual lexical simplification, organized as part of the TSAR workshop held in conjunction with EMNLP 2022. A total of 14 teams submitted the results of their lexical simplification systems for the provided test data. The results obtained for English lexical simplification were noticeably higher than those obtained for Spanish and (Brazilian) Portuguese lexical simplification.

Findings of the TSAR-2022 Shared Task on Multilingual Lexical Simplification

Lexical simplification --- which aims to simplify complex text through the replacement of difficult words using simpler alternatives while maintaining the meaning of the given text --- is popular as a way of improving text accessibility for both people and computers. First, lexical simplification through substitution can improve the understandability of complex text for, for example, non-native speakers, second language learners, and people with low literacy. Second, its usefulness has been demonstrated in many natural language processing problems like data augmentation, paraphrase generation, or word sense induction. In this paper, we investigated the applicability of existing unsupervised lexical substitution methods based on pre-trained contextual embedding models and WordNet, which incorporate Context Information, for Lexical Simplification (CILS). Although the performance of this CILS approach has been outstanding in lexical substitution tasks, its usefulness was limited at the TSAR-2022 shared task on lexical simplification. Consequently, a minimally supervised approach with careful tuning to a given simplification task may work better than unsupervised methods. Our investigation also encouraged further work on evaluating the simplicity of potential candidates and incorporating them into the lexical simplification methods.

CILS at TSAR-2022 Shared Task: Investigating the Applicability of Lexical Substitution Methods for Lexical Simplification

In this paper, we describe our approach to generate and rank candidate simplifications using pre-trained language models (Eg. BERT), publicly available word embeddings (Eg. FastText), and a part-of-speech tagger, to generate and rank candidate contextual simplifications for a given complex word. In this task, our system, PresiUniv, was placed first in the Spanish track, 5th in the Brazilian-Portuguese track, and 10th in the English track. We upload our codes and data for this project to aid in replication of our results. We also analyze some of the errors and describe design decisions which we took while writing the paper.

PresiUniv at TSAR-2022 Shared Task: Generation and Ranking of Simplification Substitutes of Complex Words in Multiple Languages

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 fine-tuned model. Among all TSAR-2022 Shared Task participants, our team was ranked second in Spanish and third in English.

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

In this paper, we describe the system we presented 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.

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

Lexical Simplification is the process of reducing the lexical complexity of a text by replacing difficult words with easier-to-read (or understand) expressions while preserving the original information and meaning. This paper explains the work done by our team "teamPN" for the English track of TSAR 2022 Shared Task of Lexical Simplification. We created a modular pipeline which combines transformers based models with traditional NLP methods like paraphrasing and verb sense disambiguation. We created a multi-level and modular pipeline where the target text is treated according to its semantics (Part of Speech Tag). The pipeline is multi-level as we utilize multiple source models to find potential candidates for replacement. It is modular as we can switch the source models and their weighting in the final re-ranking.

Divide-and-Conquer Text Simplification by Scalable Data Enhancement

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Improving Text Simplification with Factuality Error Detection

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