SIGMORPHON – U ni M orph 2023 Shared Task 0, Part 2: Cognitively Plausible Morphophonological Generalization in K orean (2023)

Abstract: This paper summarises data collection and curation for Part 2 of the 2023 SIGMORPHON-UniMorph Shared Task 0, which focused on modeling speaker knowledge and generalization of a pair of interacting phonological processes in Korean. We briefly describe how modeling the generalization task could be of interest to researchers in both Natural Language Processing and linguistics, and then summarise the traditional description of the phonological processes that are at the center of the modeling challenge. We then describe the criteria we used to select and code cases of process application in two Korean speech corpora, which served as the primary learning data. We also report the technical details of the experiment we carried out that served as the primary test data.

## introduction this paper summarises data collection and curation for part 2 of the 2023 sigmorphon-unimorph shared task 0, which focused on modeling speaker knowledge and generalization of a pair of interacting phonological processes in korean. we briefly describe how modeling the generalization task could be of interest to researchers in both natural language processing and linguistics, and then summarise the traditional description of the phonological processes that are at the center of the modeling challenge. we then describe the criteria we used to select and code cases of process application in two korean speech corpora, which served as the primary learning data. we also report the technical details of the experiment we carried out that served as the primary test data. ## description of the phonological processes we bring to bear data from the interaction of two phonological processes in korean, post-obstruent tensification (pot) and cluster simplification (cs). when their conditioning environments overlap, we can observe crucial evidence about how (or whether) the processes are ordered @xcite @xcite . note that throughout we use the international phonetic alphabet for linguistic data, augmented with the symbol "*" to indicate the tense stop series in korean; we use the symbol "c" to represent an obstruent consonant, and the symbol "v" to represent a vowel. we follow convention in the linguistic literature by using /slashes/ to represent underlying representations (urs) presumed to be represented in the speaker's mental lexicon, and [brackets] to represent surface representations (srs) which are taken to be the intended phonetic targets of phonological computation. ## task description the task was to predict human responses to a generalization task (a wug-test, cf. @xcite ), involving existing high-frequency verb stems, existing low-frequency verb stems, and novel verb stems. the stems were paired with affixes that created environments that were designed to condition pot alone (as in 막다 /mak-ta/ 'to block-decl'), cs alone (as in 밟는 /palp-n1n/ 'to step on-comp'), the critical overlapping context (as in 밟고 /palpko/ 'to step on-and'), or designed to trigger neither process, so that the underlying consonant cluster is resyllabified across the syllable boundary and survives deletion (as in 넓어 /n2lp-2/ 'to be wide-decl'). training data came in two types: a list of the 53 /-lc/-final verbs in the frequency list of korean from @xcite , and counts and hand-coding of the outcome of environments that could condition pot and/or cs in verbs from an adult-directed speech corpus and an infantdirected speech corpus. the list and corpus counts were designed to be used as the primary training data, and results of the generalization task were divided up into train, dev, and test splits. the train and dev splits were intended to be used during model development, and model performance calculated on the test set. ## corpus data collection to approximate the data that a learner of korean might be exposed to while acquiring their phonology, we culled relevant data from two corpora of spoken korean. ## experimental data collection to probe how adult speakers represent cs, pot, and their interaction in existing words, and how they generalize this knowledge to entirely novel contexts, we carried out a production task where speakers were asked to produce inflected forms of verbs, and record their productions. ## discussion as stated in section 1, the goal of this subtask was to spur collaboration and cross-talk between two communities; thus, we set aside here a discussion of the contents of the dataset, referring the reader to the paper by @xcite to summarise the findings in of the one team that worked on this subtask. the model and discussion found in their paper notwithstanding, we hope the data we gathered in this subtask may also have broader utility in testing linguistic theories of learning and representation, and in benchmarking models that attempt to reach human-like levels generalization while maintaining human-like requirements in terms of data efficiency. it is our hope that it may continue to be of use outside the context of this subtask going forward.