F ren L y S : A Tool for the Automatic Simplification of F rench General Language Texts (2021)

Abstract: Lexical simplification (LS) aims at replacing words considered complex in a sentence by simpler equivalents. In this paper, we present the first automatic LS service for French, FrenLys, which offers different techniques to generate, select and rank substitutes. The paper describes the different methods proposed by our tool, which includes both classical approaches (e.g. generation of candidates from lexical resources, frequency filter, etc.) and more innovative approaches such as the exploitation of CamemBERT, a model for French based on the RoBERTa architecture. To evaluate the different methods, a new evaluation dataset for French is introduced.

## introduction it is widely acknowledged that reading difficulties, either due to insufficient education or to mental deficiencies for example, can hinder access to information, which is likely to result in a loss of autonomy and freedom @xcite . faced with this challenge, researchers imagined applying natural language processing (nlp) to automatically transform sentences in a text in order to make it more readable, thus facilitating access to information. this is the objective pursued in the field of automatic text simplification (ats), in which the main goal is to preserve grammaticality and meaning while carrying out effective transformations to make the text simpler. ats is generally investigated at the sentence level @xcite and has therefore mostly focused on two subtasks: on the one hand, lexical simplification, described by @xcite as the task of "replacing difficult words with easy-to-read (or understand) expressions while preserving the meaning of the original text segments", on the other hand, syntactic simplification, that consists in simplifying syntactic structures in a sentence by carrying out various transformations (splitting, clause deletion, etc.). both tasks have been the subject of a great deal of research, as synthesized in @xcite ; @xcite ; @xcite ; @xcite ; @xcite ; @xcite , but most of it has been carried out on english or, to a lesser extent, on spanish. other languages are hardly represented, which is also the case for french @xcite . this why we have chosen to focus on this one. in french, ats was addressed at first at the syntactic level @xcite using rule-based systems. in parallel, lexical simplification was also investigated, based on lexicons or resources @xcite @xcite . due to the lack of training data, machine translation approaches -which are standard for english -were applied to @xcite ) only very recently, with mixed results. as a result, the situation of ats for french is clearly lagging behind that of english. the only simplification package freely available for the research community has been published recently @xcite and it remains preliminary and focused exclusively on syntax. amesure, a web platform designed to help writers of administrative texts to write in plain language (franc ¸ois et al., 2020) is more encompassing. however, it is limited to detecting complex phenomena and suggesting simplifications. in this paper, we aim to fill the gap in lexical simplification (ls) tools and resources for french by developing a tool in which several standard approaches of ls are available and by building a reference dataset to evaluate our results. this library, called frenlys for french library for simplification, follows the ls process first described by @xcite as a sequence of four steps: identifying complex terms, generating candidates for substitution, selecting the best candidates, and ranking them according to their degree of readability. our work draws from similar packages in other languages, such as lexenstein @xcite for english, the easier tool @xcite and lexsis @xcite for spanish or the work by @xcite . the article is structured as follows. section 2 presents the state of the art of lexical simplification. in section 3, we describe the different approaches we implemented for each of the ls steps. section 4 describes the methodology used to evaluate our approaches, which includes a new reference dataset for french. in section 5 we report and discuss the performance of each of these approaches on our evaluation dataset. ## related work the task of lexical simplification was first investigated by @xcite who exploited a rather simple solution: they obtained candidates for substitution using wordnet synonyms @xcite and ranked them according to their frequency. as a result of this work, researchers tried to improve different aspects of this process, either by collecting synonyms @xcite , or by ranking the candidates @xcite , etc. in his survey of the field, shardlow (2014) provided a clear view of the different challenges within ls, identifying four steps in which recent work can be classified. ## proposed approach our system is the first to offer several methods for generating candidates for substitution, selecting them based on semantic similarity with the target word and ranking them by difficulty in french. most of these methods have been previously applied to english, and we adapted them to the case of french. a few are new. all of them are described hereafter. it should be noted that frenlys does not implement any complex word identification algorithm. we believe this is a very complex task, which should be addressed as a whole and actually is @xcite , especially because cwi requires to take into account the readers' characteristics. methods based on lexical characteristics or word lists overlook the reader's characteristics and @xcite have rightly stressed that current approaches offer the same substitutions regardless of users. this tool is therefore based on the prerequisite that complex words already have been identified. for the sake of the evaluation of our tool, we relied on a manual annotation of complex words in our test set (see section 4). ## evaluation process it is usual to create evaluation corpora with the help of human annotators but this requires time and lots of annotators, which may also overlook some valid synonyms. therefore, we opted for a hybrid approach, i.e. we chose to use our synonym generation methods to propose an exhaustive list of synonyms and then we called upon annotators to select them in context and rank them according to their difficulty. the advantage of this approach is that it combines several methods from very different generations, including a synonym dictionary that was created from propositions submitted by humans @xcite . in this way, we still collect data made by humans but a priori. we explain the corpus creation process in the next section before discussing the evaluation measures we used. ## conclusion to conclude, we described the first tool for french lexical simplification that carries out three of the four classic ls steps. our tool, frenlys, will be made available to the scientific community via a freely accessible web service 5 . frenlys includes five synonym generators, based on the two principal approaches in the field: using a resource and querying embeddings. whereas @xcite had concluded that using resyf as a resource was able to outperform the approach of @xcite , we found that relying on fasttext was more efficient. however, our best method combines a synonym database with camembert as a way to filter inappropriate synonyms in context. these two sources bring information about the complex word semantic (resyf ) and its context (camembert), which comes close to the twofold strategy of @xcite . they indeed generate synonyms based on one sentence in which the complex is masked (contextual information) and the same sentence in the complex word is present in order to keep the semantic information conveyed by the complex word. frenlys offers three of them and the results showed that using a simple pos filter is sufficient to improve the f1 of our generators. ranking synonyms can be done through three techniques, the best of which integrates 21 word characteristics into a svm ranker. the results obtained for ranking seem lower than those of @xcite . this could be due to variations in the test data, but maybe also to the use of a neural classifier. we plan to improve our ranking algorithm using a neural ranker in the future to investigate this issue. finally, in addition to the implementation of the first complete ls tool for french, this paper also proposes the first evaluation dataset for french ls. this dataset will be distributed through the same web site as the api foot_6 . we hope that the availability of both resources could help boosting current ls research in french, which lacks behind similar research for other european languages.