Bayesian inference as a viable cross-linguistic word segmentation strategy: It's all about what's useful

Bayesian inference as a viable cross-linguistic word segmentation strategy: It’s all about what’s useful Lawrence Phillips and Lisa Pearl Department of Cognitive Sciences University of California, Irvine {lawphill, lpearl}@uci.edu Abstract Proposals for early segmentation strategies have centered on language-independent cues that do not need to be derived from knowledge of existing words, such as transitional prob- ability between syllables (Saffran, Aslin, & Newport, 1996). Experimental evidence also suggests that statistical cues like transitional probability are used earlier than language-specific cues like metrical stress (Thiessan & Saffran, 2003). Identifying useful items from fluent speech is one of the first tasks children must accomplish during language acquisi- tion. Typically, this task is described as word segmentation, with the idea that words are the basic useful unit that scaf- folds future acquisition processes. However, it may be that other useful items are available and easy to segment from fluent speech, such as sub-word morphology and meaning- ful word combinations. A successful early learning strategy for identifying words in English is statistical learning, imple- mented via Bayesian inference (Goldwater, Griffiths, & John- son, 2009; Pearl, Goldwater, & Steyvers, 2011; Phillips & Pearl, 2012). Here, we test this learning strategy on child- directed speech from seven languages, and discover it is ef- fective cross-linguistically, especially when the segmentation goal is expanded to include these other kinds of useful units. We also discuss which useful units are easy to segment from the different languages using this learning strategy, as the use- ful unit varies across languages. Keywords: language acquisition; Bayesian learning; word segmentation; cross-linguistic; segmentation metrics Bayesian inference for early statistical word segmentation has been shown to be successful for identifying words in English, whether the salient perceptual units are phonemes (Goldwater et al., 2009; Pearl et al., 2011) or syllables (Phillips & Pearl, 2012), and whether the inference process is optimal (Goldwater et al., 2009; Pearl et al., 2011) or constrained by cognitive limitations that children may share (Pearl et al., 2011; Phillips & Pearl, 2012). Notably, however, there is little evidence that current Bayesian word segmen- tation approaches succeed cross-linguistically (though see Johnson, 2008 and Fourtassi et al., 2013 for some examples). Introduction Segmenting useful items, typically words, from fluent speech is one of the first tasks children face in learning their native language. The earliest evidence of infant word segmentation comes at six months (Bortfeld, Morgan, Golinkoff, & Rath- bun, 2005) when familiar names are used to segment adjacent words. By seven and a half months, infants are beginning to segment words using the most common stress pattern in their language (Jusczyk, Cutler, & Redanz, 1993; Jusczyk & Aslin, 1995; Echols, Crowhurst, & Childers, 1997), and by nine months infants also utilize phonotactics (Mattys, Jusczyk, & Luce, 1999), metrical stress patterns (Morgan & Saf- fran, 1995), and coarticulation effects (Johnson & Jusczyk, 2001) to identify words. Importantly, these later segmen- tation strategies use cues that vary cross-linguistically (e.g., metrical stress: English words tend to have word-initial stress while French words tend to have word-final stress). In order to identify the relevant cues for these strategies, infants need a pool of words from which to learn the language-specific cue. While knowing some words is necessary to infer useful language-specific cues to word segmentation, there are other units that may be useful to segment. For example, sub- word morphology can be useful for grammatical categoriza- tion (e.g., -ing for identifying the word as a verb). Sim- ilarly, meaningful word combinations could be useful for early structure learning (e.g., could+I functioning as a kind of yes/no question marker). So, while it is important to know how children could segment words, it is likely that segment- ing other units is helpful for acquisition. If Bayesian segmentation is meant to be a universal early strategy, cross-linguistic success is crucial. Interestingly, there is some evidence that English may be inherently eas- ier to segment into words than other languages (Fourtassi, B¨orschinger, Johnson, & Dupoux, 2013). We therefore evalu- ate the Bayesian learners of Phillips & Pearl (2012) on seven languages with different linguistic profiles: English, German, Spanish, Italian, Farsi, Hungarian, and Japanese. Because ex- perimental evidence suggests that infants younger than seven and a half months categorically represent syllable-like units, but not phonemes (Jusczyk & Derrah, 1987; Eimas, 1999) and that phonological representations are still developing at this age (Werker & Tees, 1984), we follow previous model- ing studies (Swingley, 2005; Gambell & Yang, 2006; Lignos & Yang, 2010; Phillips & Pearl, 2012) and assume that the relevant perceptual units for word segmentation are syllables. We show that Bayesian segmentation is indeed a success- ful cross-linguistic learning strategy, especially if we define success in a more practical way than previous segmentation studies have done. In particular, we consider a segmenta- tion strategy successful if it identifies units useful for subse- quent language acquisition processes (e.g., grammatical cat- egorization, structure learning). Thus, not only are the or- thographic words traditionally used as the “gold standard” in word segmentation tasks acceptable (e.g., in “I am eating in the kitchen”: the orthographic words are I, am, eating, in, the, and kitchen), but also productive morphology (e.g., -ing) and coherent chunks made up of multiple function words (e.g.,