Modeling infant segmentation of two morphologically diverse languages (2018)

Abstract: A rich literature explores unsupervised segmentation algorithms infants could use to parse their input, mainly focusing on English, an analytic language where word, morpheme, and syllable boundaries often coincide. Synthetic languages, where words are multi-morphemic, may present unique difficulties for segmentation. Our study tests corpora of two languages selected to differ in the extent of complexity of their morphological structure, Chintang and Japanese. We use three conceptually diverse word segmentation algorithms and we evaluate them on both word- and morpheme-level representations. As predicted, results for the simpler Japanese are better than those for the more complex Chintang. However, the difference is small compared to the effect of the algorithm (with the lexical algorithm outperforming sub-lexical ones) and the level (scores were lower when evaluating on words versus morphemes). There are also important interactions between language, model, and evaluation level, which ought to be considered in future work.

## introduction human infants are known to acquire a comprehension vocabulary of hundreds of words by two years of age @xcite , and probably accumulate a protolexicon consisting solely of word forms, with no meaning attached, by the end of the first year @xcite . infants' discovery of basic units in their input has been modeled as follows. a transcript of infant-directed speech is converted into phonological text, word boundaries are removed, and an algorithm is applied. then, the word boundaries posited by the algorithm (and the resulting word tokens) are compared against the adult segmentation found in the original corpus. by and large, two classes of algorithms have been heavily studied @xcite . algorithms in the lexical class are built to find the most economical system of minimal units needed to reproduce the input. those in the sub-lexical class aim to find local cues allowing the learner to posit boundaries, for instance detectable via a dip in transitional probabilities. we will discuss both in more detail below, but for now it suffices to say that both are plausible given known infant experimental data @xcite . the present study represents the first systematic attempt to apply both types of unsupervised word form discovery techniques to two morphologically diverse languages. in the next section, we summarize previous work with the lens of morphological distinctions. ## results recall that our main research question is whether there are differences in segmentability across languages as a function of morphological complexity, particularly as the more complex language (chintang) may be oversegmented when the output is evaluated at the level of words (rather than morphemes) and when using a lexical algorithm (agu, as opposed to the sublexical dibs and tps). to answer this question, we fit a regression model to token f-scores data declaring language, level, algorithm and their interactions as independent predictors, and corpus as repeated variable (given that the corpora had been cut into 10 subparts so as to be able to carry out inferential statistics). this regression accounted for most of the variance in the data, r 2 = .90 (f (23, 216) = 98.5, @xmath0.001). as predicted, the coefficient estimating the language effect between chintang and japanese is positive (0.069), suggesting higher scores for the latter. interestingly, this effect is smaller than that of level (morpheme versus word, -0.11). the presence of all 2-and 3-way interactions, however, discourages figure 1 -token f-scores across language and representational level. models are marked by color. each "x" represents one of the ten subparts of each corpus. as clear in figure 1 there were strong interactions between all three factors (language, level, model). ## discussion when combined with results from the previous literature, we confirm the generalization that complex languages (such as the two studied here) lead to lower segmentation scores than morphologically impoverished languages (such as english). indeed, for all algorithms, we obtained lower scores for chintang and japanese than english results previously documented. we focus on the word level for this comparison, since previous work has systematically evaluated performance on this level, and not the morpheme level. for agu, we retrieve an average token f-score of .54 for japanese and .47 for chintang, whose performance is close to that for sesotho @xcite , another morphologically complex language with high degrees of synthesis. both are much lower than the .77 @xcite previously documented for english. our maximum token f-score for tp was .66, below the .85 recorded for english and even the .75 recorded for agglutinative languages by @xcite . finally, the score of .4 for dibs is similar to those recorded for russian and much lower than the score registered for english @xcite . how about the comparison between the two agglutinative languages included here ? our regression documented an advantage for the less complex japanese over the more complex chintang. although results thus far confirm the prediction that the degree of synthesis affects segmentation, several aspects of our results strongly suggest that the answer is not simple. the language effect here is small and not the same for all models. in other words, even though part of the small effect could be attributed to the fact that both languages are agglutinative, results clearly indicate that morphological complexity is not the sole determinant for word segmentation, and invite a consideration within each algorithm instead. our strongest predictions pertained agu, whose results matched our predictions well, with higher performance for japanese than chintang when evaluating on words. this language difference was reduced when evaluating on morphemes. also consistent with the proposal that agu, and probably lexical algorithms in general, are ideal to recover recombinable units is the observation that performance was overall higher for morpheme-based than word-based evaluation. readers may also notice that agu achieves the highest scores, providing further evidence to previous observations that lexical models tend to outperform sub-lexical ones @xcite . although this is a desirable feature, we point out that the fact that it is more affected by morphological variation may make it implausible as a strategy for infants learning any and all languages. as we had predicted by virtue of it being a purely phonotactic-based model, dibs is less affected by language or level differences. most token f-scores are similar to the morphologically complex russian (.35), although markedly lower than english @xcite . however, token f-scores obtained with dibs vary markedly for japanese morphemes, some of them reaching 0.5. the best explanation for these differences probably requires a recourse to phonological differences across the languages @xcite , which is orthogonal to our key question. the most complex patterns are found for prelexical tp. morphological complexity did not have a systematic effect on performance, as predicted, although we did find an interaction between subtypes, levels, and languages that is not simple to explain briefly. scores were higher for morphemes than words for all tpa. this fits in observations by gervain & erra 2012, who found that absolute-threshold tp tends to oversegment. when evaluated in morphemes, the greater number of boundaries is not a problem. contrastingly, better performance for words than morphemes for relative subtypes is not unexpected given that in this class a boundary can only be posited in relatively long strings of syllables, and thus it will tend to undersegment when evaluated in terms of morphemes. tpa may meet both desiderata of high performance and cross-linguistic validity.