Learning the Ordering of Coordinate Compounds and Elaborate Expressions in H mong, L ahu, and C hinese (2022)

Abstract: Coordinate compounds (CCs) and elaborate expressions (EEs) are coordinate constructions common in languages of East and Southeast Asia. Mortensen (2006) claims that (1) the linear ordering of EEs and CCs in Hmong, Lahu, and Chinese can be predicted via phonological hierarchies and (2) that these phonological hierarchies lack a clear phonetic rationale. These claims are significant because morphosyntax has often been seen as in a feed-forward relationship with phonology, and phonological generalizations have often been assumed to be phonetically “natural”. We investigate whether the ordering of CCs and EEs can be learned empirically and whether computational models (classifiers and sequence-labeling models) learn unnatural hierarchies similar to those posited by Mortensen (2006). We find that decision trees and SVMs learn to predict the order of CCs/EEs on the basis of phonology, beating strong baselines for all three languages, with DTs learning hierarchies strikingly similar to those proposed by Mortensen. However, we also find that a neural sequence labeling model is able to learn the ordering of elaborate expressions in Hmong very effectively without using any phonological information. We argue that EE ordering can be learned through two independent routes: phonology and lexical distribution, presenting a more nuanced picture than previous work.

## introduction in many languages of east and southeast asia, there are common constructions in which two words or phrases are coordinated without an overt marker like a conjunction @xcite . in coordinate compounds (ccs), two words are combined to form a compound word whose semantics are often a generalization of those of the two conjoined words. elaborate expressions (ees) are similar, except that they can consist of two phrases (rather than words) and include a repeated word. take the following examples: these claims have been viewed with skepticism for two reasons: morphosyntax has been widely seen as providing the inputs to phonology, not being driven by phonology @xcite ; and phonology, since @xcite and chomsky and halle (1968) has usually been seen as grounded in phonetic categories. some investigators have claimed that sound patterns that are not phonetically natural are inherently unlearnable. they can exist only as linguistic fossils deposited by a history of language change. in this paper, we undertake to investigate what kind of data is needed for (computational) learners to acquire these patterns. we report the following findings: ## theoretical significance the experiments reported in this paper have a bearing on two assumptions widely held in phonological theory: ## hypotheses based on the existing volume of work, we propose the following hypotheses: ## data we examine the ordering effects across three languages: hmong, lahu, and chinese (with middle chinese and mandarin pronunciations). for hmong, we use a list of 3253 unique elaborate expressions extracted from the 12 millionword hmong sch corpus @xcite , which was manually annotated and validated by a human expert. all of the ees are of the form 𝐴𝐵 1 𝐴𝐵 2 where 𝐵 1 𝐵 2 forms a coordinate compound. we also use the entire corpus for the ee tagging task described in section 5.2. for lahu, we use a list of 1400 ees compiled by @xcite , which contains both 𝐴𝐵 1 𝐴𝐵 2 and 𝐵 1 𝐴𝐵 2 𝐴 forms. for chinese, we use a list of 254 antonymic coordinate compounds 𝐵 1 𝐵 2 recorded in the modern chinese dictionary @xcite . middle chinese pronunciations are retrieved from wiktionary. foot_3 5 experiments ## discussion in this paper, we set out to explore the ways that the order of words in ees and ccs in hmong, lahu, and chinese can be learned by computational models. motivated by earlier linguists' findings, we first use phonological features alone to discriminate between attested and unattested orders of words. we find that in the case of all three languages, the order of words can indeed be predicted phonologically, and the "phonetically unnatural" hierarchies do predict the ordering of ees and ccs. furthermore, a decision tree classifier is able to learn more-or-less the same hierarchies, suggesting that speakers of those languages could in principle learn the linear hierarchies through exposure to the language, and use these hierarchies to decide on the correct order of words in ees and ccs. these findings provide positive evidence for hypotheses 1-3 from section 3. we then explored the ways models can utilize context and distributional patterns of words to learn the orders in the sequence tagging experiments, and we were not able to find evidence for hypothesis 4. we were surprised to find that models can perform well using only word features, and that adding phonemes to the feature set does not help at all. the seemingly contradictory results of our investigation point in an interesting direction. information on which a model could rely to learn the ordering of these constructions is present redundantly in phonology (on the one hand) and in lexical and distributional patterns (on the other). when allowed to cooperate on a level playing field, embeddings and phonology-based features both con-tribute to the identification of well-formed ees at a similar level. in other words, while it is possible that language users may use phonological hierarchies like those proposed in @xcite to select appropriate orders for ees and ccs, it is clearly not the case that they must (though they will perform a bit better if they do). these phonological hierarchies may have been more orderdefining in the history of the languages, but as the sequence tagging experiments have suggested, they may also have become fossilized in the lexicon and in distributional patterns in the modern form. many times, a (𝐵 1 , 𝐵 2 ) pair appears abundantly in multiples ees (as 𝑋𝐵 1 𝑋𝐵 2 ), as a cc (as 𝐵 1 𝐵 2 ), or in other-more complicated-discourse patterns in the same order, so that language users could learn whether a given word tends to appear in the 𝐵 1 or 𝐵 2 position. if a tagging model can learn a word representation that distinguishes between 𝐵 1 and 𝐵 2 , language users may do the same. in a sense, these results should be pleasing to both the "structure" @xcite and the "substance" @xcite camps. they show, once again, that generalizations about sounds can be robust but phonetically arbitrary. however, they leave open the possibility that the relevant synchronic generalizations are not actually phonological. ## future directions we have shown two independent routes, namely phonology and lexical distribution, by which computational methods can predict the order of words in hmong ees. a language user could probably do the same, relying on both routes to some degree when they need to select the order of words in ees. however, there is no way to know for sure without conducting a psycholinguistic experiment with native speakers, which would shed light on whether any of the modeling actually translates to human cognition. the chinese and lahu cases also raise interesting questions for future work: does the same two-route mechanism work for ees and ccs in these languages as well? answering this question will require additional data collection and annotation, but will shed significant light on this theoretically important issue. figures @xcite @xcite show what the trained decision tree looks like in the three languages. in each tree node, the top half of the box show the current majority class, attested (att) or unattested (fake), as well as the number of votes. the bottom half of the box shows the variable to branch on. as noted in the main text, a linear ordering can be induced from the tree by following the branches. take the hmong tree (figure 4 ) as an example. the first factor to split on is whether 𝐵 1 has the j tone, and if the answer is yes, the majority of words are attested (255 attested vs 15 unattested/fake). this suggests that j has a strong tendency to occur in the 𝐵 1 position, since it is the most distinguishing factor to split on. hence j can be placed as the first tone on the scale. if 𝐵 1 does not have the j tone, the next question to ask is whether 𝐵 1 has the b tone. since a yes answer again leads to majority attested words (273 attested vs 61 unattested/fake), b can be placed second on the scale. the next three questions to ask concern with the 𝐵 2 word. since a yes answer leads to attested words in all three cases, it suggests that ∅, g and s have a tendency to appear in the 𝐵 2 position, hence they can be placed on the end of the scale in that order. the next two factors concern with the j and b tones, which have already been placed on the scale, so we skip them. this process of following the left child (the no branch) and placing tones at either end of the scale is repeatedly applied, yielding the induced linear scales shown in