Automatic Interlinear Glossing for O tomi language (2021)

Abstract: In linguistics, interlinear glossing is an essential procedure for analyzing the morphology of languages. This type of annotation is useful for language documentation, and it can also provide valuable data for NLP applications. We perform automatic glossing for Otomi, an under-resourced language. Our work also comprises the pre-processing and annotation of the corpus. We implement different sequential labelers. CRF models represented an efficient and good solution for our task. Two main observations emerged from our work: 1) models with a higher number of parameters (RNNs) performed worse in our low-resource scenario; and 2) the information encoded in the CRF feature function plays an important role in the prediction of labels; however, even in cases where POS tags are not available it is still possible to achieve competitive results.

## introduction one of the important steps of linguistic documentation is to describe the grammar of a language. morphological analysis constitutes one of the stages for building this description. traditionally, this is done by means of interlinear glossing. this is an annotation task where linguists analyze sentences in a given language and they segment each word with the aim of annotating the morphosyntactic categories of the morphemes within this word (see example in this type of linguistic annotated data is a valuable resource not only for documenting a language but it can also enable nlp technologies, e.g., by providing training data for automatic morphological analyzers, taggers, morphological segmentation, etc. however, not all languages have this type of annotated corpora readily available. glossing is a sentence hí @xmath0.@xmath1: example of morpheme-by-morpheme glosses for otomi time consuming task that requires linguistic expertise. in particular, low-resource languages lack of documentation and language technologies @xcite . our aim is to successfully produce automatic glossing annotation in a low resource scenario. we focus on otomi of toluca, an indigenous language spoken in mexico (oto-manguean family). this is a morphological rich language with fusional tendency. moreover, it has scarcity of digital resources, e.g., monolingual and parallel corpora. our initial resource is a small corpus transcribed into a phonetic alphabet. we pre-process it and we perform manual glossing. once we have this dataset, we use it for training an automatic glossing system for otomi. by using different variations of conditional random fields (crfs), we were able to achieve good accuracy in the automatic glossing task (above 90%), regardless the low-resource scenario. furthermore, computationally more expensive methods, i.e., neural networks, did not perform as well. we also performed an analysis of the results from the linguistics perspective. we explored the automatic glossing performance for a subset of labels to understand the errors that the model makes. our work can be a helpful tool for reducing the workload when manually glossing. this would have an impact on language documentation. it can also lead to an increment of annotated resources for otomi, which could be a starting point for developing nlp technologies that nowadays are not yet available for this language. several works have tried to automatize this task by using computational methods. in @xcite , they use a rule-based approach (finite state transducer) to obtain glosses for plains cree, an algonquian language. they focus only on the analysis of nouns. @xcite propose a method for glossing chintang language; they divide the task into grammatical and lexical glossing. grammatical glossing is approached as a supervised part-of-speech tagging, while for lexical glossing, they use a dictionary. a fully automatized procedure is not performed since word segmentation is not addressed. some other works have approached the whole pipeline of automatic glossing as a supervised tagging task using machine learning sequential models, and they have particularly focused on underresourced languages @xcite @xcite . in @xcite , they make use of neural-based models with dual sources, they leverage easy-to-collect translations. in @xcite , they perform automatic glossing for lezgi (nakh-daghestanian family) under challenging low-resource conditions. they implement different methods, i.e., crf, crf+svm, seq2seq neural network. the best results are obtained with a crf model that leverages pos tags. the glossing is mainly focused on tagging grammatical (functional) morphemes. while the lexical items are tagged simply as stems. this latter approach especially influences our work. in fact, @xcite highlight the importance of testing these models on other languages, particularly polysynthetic languages with fusion and complex morphonology. our case of study, otomi, is precisely a language highly fusional with complex morphophonological patterns, as we will discuss on section 3. finally, automatic glossing is not only crucial for aiding linguistic research and language documentation. this type of annotation is also a valu-able source of morphological information for several nlp tasks. for instance, it could be used to train state-of-the-art morphological segmentation systems for low-resource languages @xcite . the information contained in the glosses is also helpful for training morphological reinflection systems @xcite , this consists in predicting the inflected form of a word given its lemma. it also can help in the automatic generation of morphological paradigms @xcite . these morphological tools can then be used to build downstream applications, e.g., machine translation, text generation. it is noteworthy that these are language technologies that are not yet available for all languages, especially for under-resourced ones. ## conclusion we focused on the task of automatic glossing for otomi of toluca, an indigenous language with complex morphological phenomena. we faced a lowresource scenario where we had to digitize, normalize and annotate a corpus available for this language. we applied a crf based labeler with different variations in regard to the features that were taken into account by the model. moreover, we included other sequential models, a hmm (baseline) and two rnn models. crfs outperfomed the baseline (hmm) but also the rnn models (vanilla rnn and bilstm). the crf setting that took into account more information (encoded by the feature function) had the best performance. we also noticed that excluding pos tags do not seem to harm the system's performance that much. this could be an advantage since automatic pos tagging is a resource not always available for under resourced languages. furthermore, we provided a linguistically moti-vated insight of which labels were easier to predict by our system. our automatic glossing labeler was able to achieve an accuracy of 96.2% (and 94.8% without pos tags). this sounds promising for reducing the workload when manually glossing. this can represent a middle step not only for strengthen language documentation but also for facilitating the creation of language technologies that can be useful for the speakers of otomi.