SIGMORPHON 2022 Task 0 Submission Description: Modelling Morphological Inflection with Data-Driven and Rule-Based Approaches (2022)

Abstract: This paper describes our participation in the 2022 SIGMORPHON-UniMorph Shared Task on Typologically Diverse and AcquisitionInspired Morphological Inflection Generation. We present two approaches: one being a modification of the neural baseline encoderdecoder model, the other being hand-coded morphological analyzers using finite-state tools (FST) and outside linguistic knowledge. While our proposed modification of the baseline encoder-decoder model underperforms the baseline for almost all languages, the FST methods outperform other systems in the respective languages by a large margin. This confirms that purely data-driven approaches have not yet reached the maturity to replace trained linguists for documentation and analysis especially considering low-resource and endangered languages.

## introduction there are two tracks of the task of language inflection generation: typologically diverse morphological (re-)inflection and (automatic) morphological acquisition trajectories. we only participate in the first track, typologically diverse morphological (re-)inflection. here, the main goal is to predict inflected forms of a word by given lemmas and sets of morphological tags. in total, the task features 32 languages, for several of which both a low-resource scenario and a high resource scenario are proposed. our participation was split into two systems: one is a modification of the encoder-decoder baseline described in @xcite , which is applied to all languages and resource settings. 1 the other system is based on hand-coded finite-state transducers for chukchi (ckt), upper sorbian (hsb), and kholosi (hsi). the modification of the encoder-decoder baselines aims for better interpretability of predictions, but underperforms the baseline on almost all languages. the finite-state approaches yield very strong performance on the respective languages, however, their creation may have accidentally violated the train set / test set separation by usage of publicly available data unimorph provided by @xcite while constructing the transducers. ## results the data-driven approach earned third place for both small and large languages in part one of the shared task, although under-performing the neural baseline. the official preliminary results are available in ## discussion the findings of our study follow up on the work of @xcite , where it was concluded that "it is very difficult in many cases to outperform a state-of-the-art neural network model without significant development effort and attention language result chukchi 19.565 upper sorbian 83.750 kholosi 96.667 our work similarly required a high number of working hours, but was able to outperform other systems in low-resource scenarios precisely due to the reliance on the linguistic expertise of the fst creators. the trade-off we observe in our submission is therefore how much interpretability and intuition-guided modifications of a model is desired, where for sufficiently well-documented languages the benefit of fsts may not be as obvious, but for scenarios where sufficient data may not be able to be collected, our submission would indicate that fsts still maintain an edge over neural approaches. @xcite note that for certain languages the amount of inconsistencies makes it unlikely for a hand-written grammar to surpass neural systems, where certain rules were deemed irregular enough to not warrant treatment by their fsts. we believe our study provides a partial defense for fsts with precisely the same point: in cases where the amount of data is insufficient for neural models to infer the rules of low-resource languages, it is unlikely that the neural models can perform well without further data; for rule-based approaches, even with limited amount of data (e.g. due to a lack of orthography or access to native speakers), the models can always rely on linguistic knowledge to provide working solutions. our usage of data outside of the training set is also based on this concern: it is unlikely that there will be enough human resources for most of the world's languages to have enough data collected, but for the practical situation where a morphological analyzer is nevertheless needed, our results indicate that this approach still remains to be the most practical solution. for our other submission where we experimented with a data-driven approach, we believe that it constitutes a step towards more interpretable encoder-decoder predictions, which in light of the above, may also stand as a future research direction, which could be beneficial for practical scenarios. ## conclusion we presented two different approaches to morphological inflection, a data preprocessing method to be used in conjunction with standard encoderdecoder models and hand-coded finite-state methods. despite the problems with both approaches, i.e. insufficient performance of the data-driven approach and large amounts of effort needed to engineer fsts, we think that both have their benefits, as discussed in section 4. in particular we would like to note that the effort invested into creating fsts expands computational resources for under-researched and low-resource languages and can be considered as a collaborative part in language revitalization as proposed in @xcite . also, both approaches allow for future extensions, e.g. a big improvement of finite-state analyzers would be expansion of current lexicons with guessers for assigning possible stems and part-of-speech tags.