Is Word Segmentation Child’s Play in All Languages?

Abstract

When learning language, infants need to break down the flow of input speech into minimal word-like units, a process best described as unsupervised bottom-up segmentation. Proposed strategies include several segmentation algorithms, but only cross-linguistically robust algorithms could be plausible candidates for human word learning, since infants have no initial knowledge of the ambient language. We report on the stability in performance of 11 conceptually diverse algorithms on a selection of 8 typologically distinct languages. The results are evidence that some segmentation algorithms are cross-linguistically valid, thus could be considered as potential strategies employed by all infants.

Does morphological complexity affect word segmentation? Evidence from computational modeling

“…Anonymized, 2020). Incidentally, these two strategies seem to perform quite well, and may thus be viable strategies for natural language acquisition (Loukatou, Moran, Blasi, Stoll & Cristia, 2019).…”

Is there a bilingual disadvantage for word segmentation? A computational modeling approach

“…Therefore, we adopt the Natural Language Processing/Speech Technology standard and use token recall and token precision (e.g., Ludusan, Versteegh, Jansen, Gravier, Cao, Johnson & Dupoux, 2014). This is also the approach adopted by previous work that attempts to compare the overall segmentability of different registers (childversus adult-directed speech, Cristia et al, 2019;Ludusan, Mazuka, Bernard, Cristia & Dupoux, 2017), and different languages (Caines, Altmann-Richer & Buttery, 2019;Loukatou, Stoll, Blasi & Cristia, 2018;Loukatou et al, 2019), or simply evaluate proposed algorithms (e.g., Daland & Pierrehumbert, 2011;Goldwater et al, 2009;Phillips & Pearl, 2014). These scores are calculated by comparing the output string, which contains hypothesized word breaks an algorithm supplies, against the original sentence containing word breaks.…”

“…Third, the most pressing avenue for research in modeling word segmentation involves studying more diverse languages (in the wake of Loukatou et al , 2019). Current evidence suggests sizable differences across languages, and this although only a tiny fraction of the world's languages have been investigated.…”

“…First, the comparison would be unfair because previous work that has more thoroughly explored these algorithms in English (e.g., Bernard, Thiolliere, Saksida, Loukatou, Larsen, Johnson, Fibla, Dupoux, Daland, Cao & Cristia, 2020) has found that their performance varies enormously as a function of different parameters that are used. Second, it is highly likely that the ranking in their performance varies as a function of language (and perhaps corpora) characteristics (Loukatou, Moran, Blasi, Stoll & Cristia, 2019). Therefore, any discussion of the algorithms’ performance here is limited to providing a backdrop over which to interpret effects associated to bilingual exposure.…”

Is there a bilingual disadvantage for word segmentation? A computational modeling approach

“…Additional work using adaptor grammars has suggested that they are fairly successful at segmenting child-directed speech in various languages, including German, Spanish, Italian, Farsi, Hungarian, and Japanese (Phillips & Pearl, 2014), while still showing cross-linguistic differences as you would expect across languages where the syllable structure has different levels of complexity (Fourtassi, Börschinger, Johnson & Dupoux, 2013;Johnson, 2008;Loukatou, Stoll, Blasi & Cristia, 2018).…”