•We built state-of-the-art lexical simplification systems for Spanish.•We produced new resources for lexical simplification for Spanish.•New resources improve grammaticality of simplified output.•New ...resources improve meaning preservation during simplification.•New resources increase the number and correctness of lexical changes.
The current bottleneck of all data-driven lexical simplification (LS) systems is scarcity and small size of parallel corpora (original sentences and their manually simplified versions) used for training. This is especially pronounced for languages other than English. We address this problem, taking Spanish as an example of such a language, by building new simplification-specific datasets of synonyms and paraphrases using freely available resources. We test their usefulness in the LS task by adding them, in various combinations, to the existing text simplification (TS) training dataset in a phrase-based statistical machine translation (PBSMT) approach. Our best systems significantly outperform the state-of-the-art LS systems for Spanish, by the number of transformations performed and the grammaticality, simplicity and meaning preservation of the output sentences. The results of a detailed manual analysis show that some of the newly built TS resources, although they have a good lexical coverage and lead to a high number of transformations, often change the original meaning and do not generate simpler output when used in this PBSMT setup. The good combinations of these additional resources with the TS training dataset and a good choice of language model, in contrast, improve the lexical coverage and produce sentences which are grammatical, simpler than the original, and preserve the original meaning well.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•Event-based automatic text simplification system.•Lexical and syntactic text simplification with content reduction.•No need for parallel datasets nor large set of handcrafted simplification ...rules.•Proposed simplification system is highly competitive.•Novel evaluation method based on measuring the postediting effort.
Automated Text Simplification (ATS) aims to transform complex texts into their simpler variants which are easier to understand to wider audiences and easier to process with natural language processing (NLP) tools. While simplification can be applied on lexical, syntactic, and discourse level, all previously proposed ATS systems only operated on the first two levels, thus failing at simplifying texts on the discourse level. We present a semantically-motivated ATS system which is the first system that is applied on the discourse level. By exploiting the state-of-the-art event extraction system, it is the first ATS system able to eliminate large portions of irrelevant information from texts, by maintaining only those parts of the original text that belong to factual event mentions. A few handcrafted rules ensure that the output of the system is syntactically simple, by placing each factual event mention in a separate short sentence, while the state-of-the-art unsupervised lexical simplification module, based on using word embeddings, replaces complex and infrequent words with their simpler variants. We perform a thorough evaluation, both automatic and manual, showing that our system produces more readable and simpler texts than the state-of-the-art ATS systems. Our newly proposed post-editing evaluation further reveals that our system requires less human effort for correcting grammaticality and meaning preservation on news articles than the state-of-the-art ATS system.
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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.
Text Simplification (TS) aims to convert complex sentences into their simpler variants, which are more accessible to wider audiences. Several recent studies addressed this problem as a monolingual ...machine translation (MT) problem (translating from 'original' to 'simplified' language instead of translating from one language into another) using the standard phrase-based statistical machine translation (PB-SMT) model. We investigate whether the same approach would be equally successful regardless of the type of simplification we wish to learn (given that different target audiences require different levels of simplification). Our preliminary results indicate that the standard PB-SMT model might not be able to learn the strong simplifications which are needed for certain users, e.g. people with Down syndrome. However, the phrase-tables obtained during the translation process seem to be able to capture some adequate lexical simplifications.
The detection of vague, speculative, or otherwise uncertain language has been performed in the encyclopedic, political, and scientific domains yet left relatively untouched in finance. However, the ...latter benefits from public sources of big financial data that can be linked with extracted measures of linguistic uncertainty as a mean of extrinsic model validation. Doing so further helps in understanding how the linguistic uncertainty of financial disclosures might induce financial uncertainty to the market. To explore this field, we use term weighting methods to detect linguistic uncertainty in a large dataset of financial disclosures. As a baseline, we use an existing dictionary of financial uncertainty triggers; furthermore, we retrieve related terms in specialized word embedding models to automatically expand this dictionary. Apart from an industry-agnostic expansion, we create expansions incorporating industry-specific jargon. In a set of cross-sectional event study regressions, we show that the such enriched dictionary explains a significantly larger share of future volatility, a common financial uncertainty measure, than before. Furthermore, we show that—different to the plain dictionary—our embedding models are well suited to explain future analyst forecast uncertainty. Notably, our results indicate that enriching the dictionary with industry-specific vocabulary explains a significantly larger share of financial uncertainty than an industry-agnostic expansion.
According to the official adult literacy report conducted in 24 highly-developed countries, more than 50% adults, on average, can only understand basic vocabulary, short sentences, and basic ...syntactic constructions. Everyday information found in news articles is thus inaccessible to many people, impeding their social inclusion and informed decision-making. Systems for automatic sentence simplification aim to provide scalable solution to this problem. In this paper, we propose new state-of-the-art sentence simplification systems for English and Spanish, and specifications for expert evaluation that are in accordance with well-established easy-to-read guidelines. We conduct expert evaluation of our new systems and the previous state-of-the-art systems for English and Spanish, and discuss strengths and weaknesses of each of them. Finally, we draw conclusions about the capabilities of the state-of-the-art sentence simplification systems and give some directions for future research.
Making It Simplext Saggion, Horacio; Štajner, Sanja; Bott, Stefan ...
ACM transactions on accessible computing,
06/2015, Volume:
6, Issue:
4
Journal Article
Peer reviewed
The way in which a text is written can be a barrier for many people. Automatic text simplification is a natural language processing technology that, when mature, could be used to produce texts that ...are adapted to the specific needs of particular users. Most research in the area of automatic text simplification has dealt with the English language. In this article, we present results from the Simplext project, which is dedicated to automatic text simplification for Spanish. We present a modular system with dedicated procedures for syntactic and lexical simplification that are grounded on the analysis of a corpus manually simplified for people with special needs. We carried out an automatic evaluation of the system’s output, taking into account the interaction between three different modules dedicated to different simplification aspects. One evaluation is based on readability metrics for Spanish and shows that the system is able to reduce the lexical and syntactic complexity of the texts. We also show, by means of a human evaluation, that sentence meaning is preserved in most cases. Our results, even if our work represents the first automatic text simplification system for Spanish that addresses different linguistic aspects, are comparable to the state of the art in English Automatic Text Simplification.
When two people pay attention to each other and are interested in what the other has to say or write, they almost instantly adapt their writing/speaking style to match the other. For a successful ...interaction with a user, chatbots and dialogue systems should be able to do the same. We propose a framework consisting of five psycholinguistic textual characteristics for better interaction with users. We describe the annotation processes used for collecting the data, and benchmark five binary classification tasks, experimenting with different training sizes and model architectures. The best architectures achieve macro-averaged F 1 - scores between 72% and 96% depending on the language and the task. The proposed framework proved to be fairly easy to model even with small amount of manually annotated data if right architectures are used.
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