IGT 2 P : From Interlinear Glossed Texts to Paradigms (2020)

Abstract: An intermediate step in the linguistic analysis of an under-documented language is to find and organize inflected forms that are attested in natural speech. From this data, linguists generate unseen inflected word forms in order to test hypotheses about the language’s inflectional patterns and to complete inflectional paradigm tables. To get the data linguists spend many hours manually creating interlinear glossed texts (IGTs). We introduce a new task that speeds this process and automatically generates new morphological resources for natural language processing systems: IGT-to-paradigms (IGT2P). IGT2P generates entire morphological paradigms from IGT input. We show that existing morphological reinflection models can solve the task with 21% to 64% accuracy, depending on the language. We further find that (i) having a language expert spend only a few hours cleaning the noisy IGT data improves performance by as much as 21 percentage points, and (ii) POS tags, which are generally considered a necessary part of NLP morphological reinflection input, have no effect on the accuracy of the models considered here.

## introduction over the last few years, multiple shared tasks have encouraged the development of systems for learning morphology, including generating inflected forms of the canonical form-the lemma-of a word. nlp systems that account for morphology can reduce data sparsity caused by an abundance of individual word forms in morphologically rich languages @xcite @xcite @xcite and help mitigate bias in training data for natural language processing (nlp) systems @xcite . however, such systems have often been limited to languages with publicly available structured data, i.e. languages for which tables containing inflectional patterns can be found, for example, in online dictionaries like wiktionary. 1 this limits the development of nlp systems for morphology to languages for which morphological information can be easily extracted. here, we propose to instead make use of a resource which is much more common, especially for low-resource languages: we explore how to leverage interlinear glossed text (igt)-a common artifact of linguistic field research-to generate unseen forms of inflectional paradigms, as illustrated in figure 1 . this task, which we call igt-to-paradigms (igt2p), differs from the ex-isting morphological inflection @xcite task in three aspects: (1) inflected forms extracted from igt are noisier than curated training data for morphological generation, (2) since lemmas are not explicitly identified in igt, systems cannot be trained on typical lemma-to-form mappings and, instead, must be trained on form-to-form mappings, and (3) partof-speech (pos) tags are often unavailable in igt. igt2p can thus be seen as a noisy version of morphological reinflection @xcite , but without explicit pos information. our experiments show that morphological reinflection systems following preprocessing are strong baselines for this task. we further perform two analyses: we examine which inflection model performs better on noisy and cleaned igt data and how the performance varies across languages and data quality or size. ## conclusion we proposed a new morphological generation task called igt2p, which aims to learn inflectional paradigmatic patterns from interlinear gloss texts (igt) produced in linguistics fieldwork. we experimented with neural models that have been used for morphological reinflection and new preprocessing steps as baselines for the task. our experiments show that igt2p is a promising method for creating new morphological resources in a wide range of low-resource languages. with sufficient igt annotations, igt2p obtains reasonable performance from noisy data. we investigated the effect of manual cleaning on model performance and showed that even very limited cleaning effort (2-7 hours) drastically improves results. the inherent noisiness in igt and other linguistic field data can be overcome with limited input from domain experts. this is a significant contribution considering the extensive effort-on the order of months and years-to produce the curated structured data normally used to train nlp models. in languages with the noisiest data performance is improved even further by data augmentation techniques. finally, since field data does not often include pos annotation, we investigated the usefulness of pos tags for morphological reinflection and find that, surprisingly and in contrast to common assumptions, they are not beneficial to recent state-of-the-art systems. this is a useful discovery for researchers who wish to optimize their inflection systems. there is room for future improvement. better techniques for further cleaning might be useful since accuracy seems to have close related to data quality. however, at some point more cleaning will return less improvement. upper bounds could be established by comparing results on languages with gold standard inflection tables, although polysynthetic languages like arapaho would make this difficult since their tables do not always include noun incorporation. better use of experts' time might involve identification of lemmata that could be used to train a lemma-to-form model, rather than the form-to-form mapping used here. another approach would be to compare improvements between manual-only cleaning and cleaning done by a linguist working with someone who can write scripts to automatically correct repeated patterns of noise.