Lexical Simplification Research Articles

Is consecutive interpreting easier than simultaneous interpreting? – a corpus-based study of lexical simplification in interpretation

ABSTRACTLexical simplification parameters, labeled as representative of the cognitive load in various processes, have been applied in corpus-based studies on translation universals and interpreting outputs. We speculate that the output of simultaneous interpreting (SI), the extreme situation of language control, might be more simplified than that of consecutive interpreting (CI) due to the high cognitive load. To test this hypothesis, the present study examines the simplification patterns of rendered texts, based on a corpus composed of SI and CI output texts, read-out translated speeches and non-interpreted, original English speeches in three dimensions: information density, lexical repetitiveness and lexical sophistication. The results demonstrate that all of the parameters apply more to CI than to SI, indicating that the CI output is more simplified than the SI output. This pattern of results implies that the cognitive load of CI, if not higher, may be as high as that of SI. The research reported here is the first to compare quantitatively the lexical features of the output of CI versus SI. The counterintuitive results lend support to the modification of the established Effort Model of CI.

Improving lexical coverage of text simplification systems for Spanish

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.

Improving Consumer Understanding of Medical Text: Development and Validation of a New SubSimplify Algorithm to Automatically Generate Term Explanations in English and Spanish.

BackgroundWhile health literacy is important for people to maintain good health and manage diseases, medical educational texts are often written beyond the reading level of the average individual. To mitigate this disconnect, text simplification research provides methods to increase readability and, therefore, comprehension. One method of text simplification is to isolate particularly difficult terms within a document and replace them with easier synonyms (lexical simplification) or an explanation in plain language (semantic simplification). Unfortunately, existing dictionaries are seldom complete, and consequently, resources for many difficult terms are unavailable. This is the case for English and Spanish resources.ObjectiveOur objective was to automatically generate explanations for difficult terms in both English and Spanish when they are not covered by existing resources. The system we present combines existing resources for explanation generation using a novel algorithm (SubSimplify) to create additional explanations.MethodsSubSimplify uses word-level parsing techniques and specialized medical affix dictionaries to identify the morphological units of a term and then source their definitions. While the underlying resources are different, SubSimplify applies the same principles in both languages. To evaluate our approach, we used term familiarity to identify difficult terms in English and Spanish and then generated explanations for them. For each language, we extracted 400 difficult terms from two different article types (General and Medical topics) balanced for frequency. For English terms, we compared SubSimplify’s explanation with the explanations from the Consumer Health Vocabulary, WordNet Synonyms and Summaries, as well as Word Embedding Vector (WEV) synonyms. For Spanish terms, we compared the explanation to WordNet Summaries and WEV Embedding synonyms. We evaluated quality, coverage, and usefulness for the simplification provided for each term. Quality is the average score from two subject experts on a 1-4 Likert scale (two per language) for the synonyms or explanations provided by the source. Coverage is the number of terms for which a source could provide an explanation. Usefulness is the same expert score, however, with a 0 assigned when no explanations or synonyms were available for a term.ResultsSubSimplify resulted in quality scores of 1.64 for English (P<.001) and 1.49 for Spanish (P<.001), which were lower than those of existing resources (Consumer Health Vocabulary [CHV]=2.81). However, in coverage, SubSimplify outperforms all existing written resources, increasing the coverage from 53.0% to 80.5% in English and from 20.8% to 90.8% in Spanish (P<.001). This result means that the usefulness score of SubSimplify (1.32; P<.001) is greater than that of most existing resources (eg, CHV=0.169).ConclusionsOur approach is intended as an additional resource to existing, manually created resources. It greatly increases the number of difficult terms for which an easier alternative can be made available, resulting in greater actual usefulness.

Text Simplification Using Consumer Health Vocabulary to Generate Patient-Centered Radiology Reporting: Translation and Evaluation.

BackgroundRadiology reporting is a clinically oriented form of documentation that reflects critical information for patients about their health care processes. Realizing its importance, many medical institutions have started providing radiology reports in patient portals. The gain, however, can be limited because of medical language barriers, which require a way for customizing these reports for patients. The open-access, collaborative consumer health vocabulary (CHV) is a terminology system created for such purposes and can be the basis of lexical simplification processes for clinical notes.ObjectiveThe aim of this study was to examine the comprehensibility and suitability of CHV in simplifying radiology reports for consumers. This was done by characterizing the content coverage and the lexical similarity between the terms in the reports and the CHV-preferred terms.MethodsThe overall procedure was divided into the following two main stages: (1) translation and (2) evaluation. The translation process involved using MetaMap to link terms in the reports to CHV concepts. This is followed by replacing the terms with CHV-preferred terms using the concept names and sources table (MRCONSO) in the Unified Medical Language System (UMLS) Metathesaurus. In the second stage, medical terms in the reports and general terms that are used to describe medical phenomena were selected and evaluated by comparing the words in the original reports with the translated ones. The evaluation includes measuring the content coverage, investigating lexical similarity, and finding trends in missing concepts.ResultsOf the 792 terms selected from the radiology reports, 695 of them could be mapped directly to CHV concepts, indicating a content coverage of 88.5%. A total of 51 of the concepts (53%, 51/97) that could not be mapped are names of human anatomical structures and regions, followed by 28 anatomical descriptions and pathological variations (29%, 28/97). In addition, 12 radiology techniques and projections represented 12% of the unmapped concepts, whereas the remaining six concepts (6%, 12/97) were physiological descriptions. The rate of lexical similarity between the CHV-preferred terms and the terms in the radiology reports was approximately 72.6%.ConclusionsThe CHV covered a high percentage of concepts found in the radiology reports, but unmapped concepts are associated with areas that are commonly found in radiology reporting. CHV terms also showed a high percentage of lexical similarity with terms in the reports, which contain a myriad of medical jargon. This suggests that many CHV terms might not be suitable for lay consumers who would not be facile with radiology-specific vocabulary. Therefore, further patient-centered content changes are needed of the CHV to increase its usefulness and facilitate its integration into consumer-oriented applications.

A Survey on Lexical Simplification

Lexical Simplification is the process of replacing complex words in a given sentence with simpler alternatives of equivalent meaning. This task has wide applicability both as an assistive technology for readers with cognitive impairments or disabilities, such as Dyslexia and Aphasia, and as a pre-processing tool for other Natural Language Processing tasks, such as machine translation and summarisation. The problem is commonly framed as a pipeline of four steps: the identification of complex words, the generation of substitution candidates, the selection of those candidates that fit the context, and the ranking of the selected substitutes according to their simplicity. In this survey we review the literature for each step in this typical Lexical Simplification pipeline and provide a benchmarking of existing approaches for these steps on publicly available datasets. We also provide pointers for datasets and resources available for the task.

Effects of Lexical Simplification and Elaboration on Iranian Intermediate EFL Learners’ Learning and Retention of Phrasal Verbs

This study aimed at investigating the effect of lexical simplification and elaboration on Iranian intermediate EFL learners’ learning and retention of phrasal verbs. In order to achieve this goal, a PET test was run into 130 learners. When target participants were chosen, they were assigned into three groups including two treatment groups and one control group. Through a multiple choice test, 40 out of 60 phrasal verbs were selected as target phrasal verbs. Through 8 sessions of treatment, each group received a reading comprehension text that contained 5 phrasal verbs. But the way the phrasal verbs in two experimental groups were modified was different. One of them received elaborated input and the other group received simplified input; meanwhile, the control group received unmodified input. After eight sessions of treatment, an immediate multiple choice test was administered to find out which techniques had stronger effect on participant’s learning and retention of phrasal verbs. After one month, the same test, with a different order of items, was administered as delayed posttest to check the participants’ level of retention. According to the results of the study simplified input group significantly outperformed the control and elaborated group on both learning and retention of phrasal verbs.

The Effect of Reducing Lexical and Syntactic Complexity of Texts on Reading Comprehension

The present study investigated the effect of different types of text simplification (i.e., reducing the lexical and syntactic complexity of texts) on reading comprehension of English as a Foreign Language learners (EFL). Sixty female intermediate EFL learners from three intact classes in Tabarestan Language Institute in Tehran participated in the study. The intact classes were assigned to three experimental groups. Moreover, to homogenize the groups, the researchers administered a general proficiency test (TOEFL, 2003) to the participants. The results revealed no significant difference among the groups in general proficiency and reading ability. Then four reading comprehension texts from TOEFL test (2005) were simplified through lexical simplification, syntactic simplification or lexical-syntactic simplification techniques. The simplified texts, along with their reading comprehension (RC) questions, formed the three versions of the post-test, each version contained either lexically, syntactically or lexical-syntactically simplified texts. Each group took one version of the post-test. The scores were analyzed through one-way ANOVA. The results revealed a significant difference among the groups. The post hoc test indicated that the lexical-syntactic simplification group significantly outperformed the lexical simplification group and performed considerably better than the syntactic simplification group. There was no significant difference between the lexical and syntactic simplification groups, although the latter showed better results.

Una proposta multimodale per il marketing premozionale dell’Apulianluxury: la Puglia come espressione del turismo di lusso responsabile

Abstract – This study introduces an innovative ‘premotional’ marketing strategy for the emotional promotion of responsible luxury tourism in Apulia, labelled as “#Apulianluxury”, which has been developed for this specific research to increase the appeal of the region by focusing on its natural, social, and cultural properties. In particular, this paper will illustrate the cognitive and linguistic grounds of a number of premotional messages that aim at attracting potential receivers by activating in them an alternative perception of the holiday as an experience of cultural and personal growth. Such perception is stimulated by the interaction between the ‘luxury tourism’ and ‘responsible tourism’ schemata, which leads to the ‘expected’/‘unexpected’ cognitive oppositions in the examined advertisements. The analysis of the selected corpus of multimodal texts will enquire into the extent to which the lexical, structural and pragmatic attributes of English as a lingua franca interact with the visual features of the messages so as to provide the alternative view of the holiday, and increase the cross-cultural accessibility of this premotional campaign. In fact, the connection between the lexical and syntactic simplification strategies and the visual representations allow tourists to trigger the pragmatic inferences that determine the appropriate identification and interpretation of the illocutionary force and perlocutionary effects pursued by the Apulian characterization of responsible luxury tourism under discussion

Leveraging event-based semantics for automated text simplification

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.

A New Scenario of Machine Translation: Dynamic Contextual Effects with Diverse Paratextual Application

Not many people want to read an incomprehensible machine-created translation (MT) even when it is accessed for free on the web. Thus, pre-editing is done to improve the readability of the MT output. However, syntactic and lexical simplification of pre-editing does not explicate the implicit meanings of culture-specific words and messages. To compensate for the simplification-resulted flaw, this paper proposes the use of some paratexts such as notes and pictures to create a better contextual effect in the MT text. The pragmatic function of paratexts can be justified by framing it within the framework of Gutt’s relevance-theoretic concepts. Breaking free from the unified contextual effect, this paper supports dynamic contextual effect and illustrates how weak, fair and strong contextual effects are created by comparing two sets of three English MT texts that consist of an MT without paratexts, an MT with added notes and an MT with added notes and pictures. The pragmatic significance of this research shows that paratexts play a useful role in MT-enabled communication and that the application of paratextual aids enhances MT audience’s comprehension based on the dynamic contextual effect created in the new MT scenario.

From EPIC to EPTIC — Exploring simplification in interpreting and translation from an intermodal perspective

Abstract This article introduces EPTIC (the European Parliament Translation and Interpreting Corpus), a new bidirectional (English&lt;&gt;Italian) corpus of interpreted and translated EU Parliament proceedings. Built as an extension of the English&lt;&gt;Italian subsection of EPIC (the European Parliament Interpreting Corpus), EPTIC is an intermodal corpus featuring the pseudo-parallel outputs of interpreting and translation processes, aligned to each other and to the corresponding source texts (speeches by MEPs and their written up versions). As a first attempt at unearthing the potential of EPTIC, we investigate lexical simplification replicating the methodology proposed by Laviosa (1998a; 1998b), but extending it to encompass both a monolingual comparable and an intermodal perspective. Our results indicate that the mediation process reduces complexity in both modes of language production and both language directions, with interpreters simplifying the input more than translators, and evidence of simplification being more lexical in English and more lexico-syntactic in Italian.

Unsupervised Lexical Simplification for Non-Native Speakers

Lexical Simplification is the task of replacing complex words with simpler alternatives. We propose a novel, unsupervised approach for the task. It relies on two resources: a corpus of subtitles and a new type of word embeddings model that accounts for the ambiguity of words. We compare the performance of our approach and many others over a new evaluation dataset, which accounts for the simplification needs of 400 non-native English speakers. The experiments show that our approach outperforms state-of-the-art work in Lexical Simplification.

Text Simplification Using Neural Machine Translation

Text simplification (TS) is the technique of reducing the lexical, syntactical complexity of text. Existing automatic TS systems can simplify text only by lexical simplification or by manually defined rules. Neural Machine Translation (NMT) is a recently proposed approach for Machine Translation (MT) that is receiving a lot of research interest. In this paper, we regard original English and simplified English as two languages, and apply a NMT model–Recurrent Neural Network (RNN) encoder-decoder on TS to make the neural network to learn text simplification rules by itself. Then we discuss challenges and strategies about how to apply a NMT model to the task of text simplification.

What do pauses in narrative production reveal about the nature of word retrieval deficits in PPA?

Naming and word-retrieval deficits, which are common characteristics of primary progressive aphasia (PPA), differentially affect production across word classes (e.g., nouns, verbs) in some patients. Individuals with the agrammatic variant (PPA-G) often show greater difficulty producing verbs whereas those with the semantic variant (PPA-S) show greater noun deficits and those with logopenic PPA (PPA-L) evince no clear-cut differences in production of the two word classes. To determine the source of these production patterns, the present study examined word-finding pauses as conditioned by lexical variables (i.e., word class, frequency, length) in narrative speech samples of individuals with PPA-S (n=12), PPA-G (n=12), PPA-L (n=11), and cognitively healthy controls (n=12). We also examined the relation between pause distribution and cortical atrophy (i.e., cortical thickness) in nine left hemisphere regions of interest (ROIs) linked to word production. Results showed higher overall pause rates for PPA compared to unimpaired controls; however, greater naming severity was not associated with increased pause rate. Across all groups, more pauses were produced before lower vs. higher frequency words, with no independent effects of word length after controlling for frequency. With regard to word class, the PPA-L group showed a higher rate of pauses prior to production of nouns compared to verbs, consistent with noun-retrieval deficits arising at the lemma level of word production. Those with PPA-G and PPA-S, like controls, produced similar pause rates across word classes; however, lexical simplification (i.e., production of higher-frequency and/or shorter words) was evident in the more-impaired word class: nouns for PPA-S and verbs for PPA-G. These patterns are consistent with conceptual and/or lemma-level impairments for PPA-S, predominantly affecting objects/nouns, and a lemma-level verb-retrieval deficit for PPA-G, with a concomitant impairment in phonological encoding and articulation affecting overall pause rates. The greater tendency to pause before nouns was correlated with atrophy in the left precentral gyrus, inferior frontal gyrus and inferior parietal lobule, whereas the greater tendency to pause before less frequent and longer words was associated with atrophy in left precentral and inferior parietal regions.

Making It Simplext

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.

Simplifying words in context. Experiments with two lexical resources in Spanish

In this paper we study the effect of different lexical resources for selecting synonyms and strategies for word sense disambiguation in a lexical simplification system for the Spanish language. The resources used for the experiments are the Spanish EuroWordNet, the Spanish Open Thesaurus and a combination of both. As for the synonym selection strategies, we have used both local and global contexts for word sense disambiguation. We present a novel evaluation framework in lexical simplification that takes into account the level of ambiguity of the word to be simplified. The evaluation compares various instances of the lexical simplification system, a gold standard, and a baseline. The paper presents an in-depth qualitative error analysis of the results.

Towards Automatic Text Adaptation in Russian

This article describes ways of using original texts in the National Russian Corpus and news texts to teach Russian as a foreign language. The two-year work of a scientific group from the Higher School of Economics (from Nizhny Novgorod and Moscow), called CorpLings was analyzed. Special attention was paid to the automatic adaptation of acute news texts, which was the basic principle of the research part of the project. We also describe ways of simplifying syntactical and morphological structures that may seem difficult for students at an elementary level. The stages used for lexical simplification are described in detail, such as the creation of an algorithm to find the most appropriate synonyms based on morphological rules, and an analysis of the statistical model of words’ contextual proximity. This article also addresses the difficulties faced by developers and the final results of our research.

Distortion in Screening and Spatial Preferences

This article describes ways to use original texts in the National Russian Corpus as well as news texts for teaching Russian as a foreign language. Two-year work of a scientific group of Higher School of Economics (Nizhny Novgorod-Moscow), which is called CorpLings is analyzed. Special attention is paid to the basic principles of research part of the project – automatic adaptation of acute news texts. We also describe ways to simplify structures, which seem difficult for students at the elementary level, from the point of view of their syntactic and morphological structure. Stages, needed for lexical simplification are described in detail. Among them there are creation of an algorithm to find the most appropriate synonyms, based on morphological rules, and analysis of a statistical model of contextual proximity of words. The article also addresses the difficulties faced by developers and the final result of research.

Syntactic and Lexical Simplification: The Impact on EFL Listening Comprehension at Low and High Language Proficiency Levels

This study aimed at examining whether syntactic and lexical simplification affect listening comprehension at different levels. The participants were 180 female Iranian EFL students. They were learning English at an English language institute. A standardized test, namely, TOEFL was administered to choose the participants. Then the participants were divided into two groups (90 of low and 90 of high-proficiency level). The participants at high language proficiency level were randomly divided into three groups (one control group, two experimental groups). The participants at the low language proficiency level were randomly divided into three groups, too (one control group, two experimental groups). Two versions of a passage were prepared at a high language proficiency level. Two versions of a passage were prepared at a low language proficiency level, too. The obtained passages which were read by a native English teacher and recorded on a CD were played back at a normal speed rate. The participants were asked to answer the multiple-choice questions after listening to the passages. Then the answer sheets were scored. The means and the standard deviations of groups' performances were calculated. To determine whether or not there were overall significant differences between groups' performances, a t-test was separately applied within levels. The results of the t-test revealed that the groups exposed to syntactically and lexically simplified versions outperformed the other groups.

Integration of Lexical and Syntactic Simplification Capabilities in a Text Editor

Nowadays, with the rapid development of new technologies, it is very difficult to be an active member of the society unless one is proficient in reading. Unfortunately, there are a lot of people who experience reading or writing problems. Experts who create materials for these groups of people have few user-friendly tools at their disposal in order to prepare easy-to- read texts. The present work has been done with the aim of providing support for text simplification experts who prepare easy-to-read texts in English, which is why different Natural Language Processing tools have been integrated in an already existing text editor.