The ASL-CDI 2.0: an updated, normed adaptation of the MacArthur Bates Communicative Development Inventory for American Sign Language

Vocabulary is a critical early marker of language development. The MacArthur Bates Communicative Development Inventory has been adapted to dozens of languages, and provides a bird’s-eye view of children’s early vocabularies which can be informative for both research and clinical purposes. We present an update to the American Sign Language Communicative Development Inventory (the ASL-CDI 2.0, https://www.aslcdi.org), a normed assessment of early ASL vocabulary that can be widely administered online by individuals with no formal training in sign language linguistics. The ASL-CDI 2.0 includes receptive and expressive vocabulary, and a Gestures and Phrases section; it also introduces an online interface that presents ASL signs as videos. We validated the ASL-CDI 2.0 with expressive and receptive in-person tasks administered to a subset of participants. The norming sample presented here consists of 120 deaf children (ages 9 to 73 months) with deaf parents. We present an analysis of the measurement properties of the ASL-CDI 2.0. Vocabulary increases with age, as expected. We see an early noun bias that shifts with age, and a lag between receptive and expressive vocabulary. We present these findings with indications for how the ASL-CDI 2.0 may be used in a range of clinical and research settingsAccepted manuscrip

Early vocabulary development in deaf native signers: a British Sign Language adaptation of the communicative development inventories

Background: There is a dearth of assessments of sign language development in young deaf children. This study gathered age-related scores from a sample of deaf native signing children using an adapted version of the MacArthur-Bates CDI (Fenson et al., 1994). Method: Parental reports on children’s receptive and expressive signing were collected longitudinally on 29 deaf native British Sign Language (BSL) users, aged 8–36 months, yielding 146 datasets. Results: A smooth upward growth curve was obtained for early vocabulary development and percentile scores were derived. In the main, receptive scores were in advance of expressive scores. No gender bias was observed. Correlational analysis identified factors associated with vocabulary development, including parental education and mothers’ training in BSL. Individual children’s profiles showed a range of development and some evidence of a growth spurt. Clinical and research issues relating to the measure are discussed. Conclusions: The study has developed a valid, reliable measure of vocabulary development in BSL. Further research is needed to investigate the relationship between vocabulary acquisition in native and non-native signers

Domain adaptation strategies in statistical machine translation: a brief overview

© Cambridge University Press, 2015.Statistical machine translation (SMT) is gaining interest given that it can easily be adapted to any pair of languages. One of the main challenges in SMT is domain adaptation because the performance in translation drops when testing conditions deviate from training conditions. Many research works are arising to face this challenge. Research is focused on trying to exploit all kinds of material, if available. This paper provides an overview of research, which copes with the domain adaptation challenge in SMT.Peer ReviewedPostprint (author's final draft

Transfer Learning in Multilingual Neural Machine Translation with Dynamic Vocabulary

We propose a method to transfer knowledge across neural machine translation (NMT) models by means of a shared dynamic vocabulary. Our approach allows to extend an initial model for a given language pair to cover new languages by adapting its vocabulary as long as new data become available (i.e., introducing new vocabulary items if they are not included in the initial model). The parameter transfer mechanism is evaluated in two scenarios: i) to adapt a trained single language NMT system to work with a new language pair and ii) to continuously add new language pairs to grow to a multilingual NMT system. In both the scenarios our goal is to improve the translation performance, while minimizing the training convergence time. Preliminary experiments spanning five languages with different training data sizes (i.e., 5k and 50k parallel sentences) show a significant performance gain ranging from +3.85 up to +13.63 BLEU in different language directions. Moreover, when compared with training an NMT model from scratch, our transfer-learning approach allows us to reach higher performance after training up to 4% of the total training steps.Comment: Published at the International Workshop on Spoken Language Translation (IWSLT), 201