Is Word Segmentation Child’s Play in All Languages?

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 3931–3937 Florence, Italy, July 28 - August 2, 2019. c⃝2019 Association for Computational Linguistics 3931 Is word segmentation child’s play in all languages? Georgia R. Loukatou Laboratoire de Sciences Cognitives et de Psycholinguistique, D´epartement d’´etudes cognitives, ENS, EHESS, CNRS, PSL University georgialoukatou@gmail.com Steven Moran Dami´an E. Blasi Sabine Stoll University of Zurich Alejandrina Cristia Laboratoire de Sciences Cognitives et de Psycholinguistique, D´epartement d’´etudes cognitives, ENS, EHESS, CNRS, PSL University Abstract When learning language, infants need to break down the flow of input speech into minimal word-like units, a process best described as un- supervised bottom-up segmentation. Proposed strategies include several segmentation algo- rithms, but only cross-linguistically robust al- gorithms could be plausible candidates for hu- man word learning, since infants have no ini- tial knowledge of the ambient language. We report on the stability in performance of 11 conceptually diverse algorithms on a selec- tion of 8 typologically distinct languages. The results are evidence that some segmentation algorithms are cross-linguistically valid, thus could be considered as potential strategies em- ployed by all infants. 1 Introduction Six-month-old infants can recognize recurrent words in running speech, even with no mean- ing available or with experimentally impover- ished cues to wordhood (Saffran et al., 1996). Most words do not appear in isolation (Brent and Siskind, 2001), so infants would need to discover the form of words in their caregivers’ input before attaching them to meaning. Since infants do not know which language(s) will be found in their en- vironment at the beginning of development, they would be better off by using segmentation strate- gies that perform above chance for any language. In fact, despite the fact that languages vary widely in a number of dimensions affecting word segmen- tation, all human languages are learnable for in- fants (see Discussion for the question of the extent of variation in human learning). 1.1 Unsupervised bottom-up segmentation across languages The problem of learners retrieving words in in- put has a long history in computational approaches (e.g., Harris 1955; Elsner et al. 2013; Lee et al. 2015). Most previous computational research has used as input texts representing phonologized lan- guage, that is, sequences of phonemes with no overt word boundaries, and the task is to retrieve these. Several algorithms inspired by laboratory research on infant word segmentation are currently represented in WordSeg, an open source package (Bernard et al., 2018). Are such algorithms as robust to cross-linguistic variation as human infants are? Some previous work has assessed the generalizability of specific approaches across different languages, typically concluding that strong performance differences arise (Johnson 2008; Daland 2009; Gervain and Erra 2012; Fourtassi et al. 2013; Saksida et al. 2017; Loukatou et al. 2018, with the possible ex- ception of Phillips and Pearl 2014a,b). However, very little previous research compares the performance of a wide range of algorithms us- ing diverse and cognitively plausible segmentation methods within a large set of typologically diverse languages and closely …

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