Dataset Information

Lexical Learning May Contribute to Phonetic Learning in Infants: A Corpus Analysis of Maternal Spanish.

ABSTRACT: In their first year, infants begin to learn the speech sounds of their language. This process is typically modeled as an unsupervised clustering problem in which phonetically similar speech-sound tokens are grouped into phonetic categories by infants using their domain-general inference abilities. We argue here that maternal speech is too phonetically variable for this account to be plausible, and we provide phonetic evidence from Spanish showing that infant-directed Spanish vowels are more readily clustered over word types than over vowel tokens. The results suggest that infants' early adaptation to native-language phonetics depends on their word-form lexicon, implicating a much wider range of potential sources of influence on infants' developmental trajectories in language learning.

SUBMITTER: Swingley D

PROVIDER: S-EPMC7224410 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

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Lexical Learning May Contribute to Phonetic Learning in Infants: A Corpus Analysis of Maternal Spanish.

Swingley Daniel D Alarcon Claudia C

Cognitive science 20180521

In their first year, infants begin to learn the speech sounds of their language. This process is typically modeled as an unsupervised clustering problem in which phonetically similar speech-sound tokens are grouped into phonetic categories by infants using their domain-general inference abilities. We argue here that maternal speech is too phonetically variable for this account to be plausible, and we provide phonetic evidence from Spanish showing that infant-directed Spanish vowels are more read ...[more]

PMID: 29785714

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Project description: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.

Dataset Information

Lexical Learning May Contribute to Phonetic Learning in Infants: A Corpus Analysis of Maternal Spanish.

Publications

Lexical Learning May Contribute to Phonetic Learning in Infants: A Corpus Analysis of Maternal Spanish.

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