Yet Another Format of Universal Dependencies for Korean

In this study, we propose a morpheme-based scheme for Korean dependency parsing and adopt the proposed scheme to Universal Dependencies. We present the linguistic rationale that illustrates the motivation and the necessity of adopting the morpheme-based format, and develop scripts that convert between the original format used by Universal Dependencies and the proposed morpheme-based format automatically. The effectiveness of the proposed format for Korean dependency parsing is then testified by both statistical and neural models, including UDPipe and Stanza, with our carefully constructed morpheme-based word embedding for Korean. morphUD outperforms parsing results for all Korean UD treebanks, and we also present detailed error analyses.Comment: COLING2022, Poste

Crowdsourcing Question-Answer Meaning Representations

We introduce Question-Answer Meaning Representations (QAMRs), which represent the predicate-argument structure of a sentence as a set of question-answer pairs. We also develop a crowdsourcing scheme to show that QAMRs can be labeled with very little training, and gather a dataset with over 5,000 sentences and 100,000 questions. A detailed qualitative analysis demonstrates that the crowd-generated question-answer pairs cover the vast majority of predicate-argument relationships in existing datasets (including PropBank, NomBank, QA-SRL, and AMR) along with many previously under-resourced ones, including implicit arguments and relations. The QAMR data and annotation code is made publicly available to enable future work on how best to model these complex phenomena.Comment: 8 pages, 6 figures, 2 table

On understanding character-level models for representing morphology

Morphology is the study of how words are composed of smaller units of meaning (morphemes). It allows humans to create, memorize, and understand words in their language. To process and understand human languages, we expect our computational models to also learn morphology. Recent advances in neural network models provide us with models that compose word representations from smaller units like word segments, character n-grams, or characters. These so-called subword unit models do not explicitly model morphology yet they achieve impressive performance across many multilingual NLP tasks, especially on languages with complex morphological processes. This thesis aims to shed light on the following questions: (1) What do subword unit models learn about morphology? (2) Do we still need prior knowledge about morphology? (3) How do subword unit models interact with morphological typology? First, we systematically compare various subword unit models and study their performance across language typologies. We show that models based on characters are particularly effective because they learn orthographic regularities which are consistent with morphology. To understand which aspects of morphology are not captured by these models, we compare them with an oracle with access to explicit morphological analysis. We show that in the case of dependency parsing, character-level models are still poor in representing words with ambiguous analyses. We then demonstrate how explicit modeling of morphology is helpful in such cases. Finally, we study how character-level models perform in low resource, cross-lingual NLP scenarios, whether they can facilitate cross-linguistic transfer of morphology across related languages. While we show that cross-lingual character-level models can improve low-resource NLP performance, our analysis suggests that it is mostly because of the structural similarities between languages and we do not yet find any strong evidence of crosslinguistic transfer of morphology. This thesis presents a careful, in-depth study and analyses of character-level models and their relation to morphology, providing insights and future research directions on building morphologically-aware computational NLP models