The IMS – CUB oulder System for the SIGMORPHON 2020 Shared Task on Unsupervised Morphological Paradigm Completion (2020)

Abstract: In this paper, we present the systems of the University of Stuttgart IMS and the University of Colorado Boulder (IMS–CUBoulder) for SIGMORPHON 2020 Task 2 on unsupervised morphological paradigm completion (Kann et al., 2020). The task consists of generating the morphological paradigms of a set of lemmas, given only the lemmas themselves and unlabeled text. Our proposed system is a modified version of the baseline introduced together with the task. In particular, we experiment with substituting the inflection generation component with an LSTM sequence-to-sequence model and an LSTM pointer-generator network. Our pointer-generator system obtains the best score of all seven submitted systems on average over all languages, and outperforms the official baseline, which was best overall, on Bulgarian and Kannada.

## introduction in recent years, a lot of progress has been made on the task of morphological inflection, which consists of generating an inflected word, given a lemma and a list of morphological features @xcite @xcite @xcite . the systems developed for this task learn to model inflection in morphologically complex languages in a supervised fashion. however, not all languages have annotated data available. for the 2018 sigmorphon shared task @xcite , data for 103 unique languages has been provided. even this highly multilingual dataset is just covering 1.61% of the 6359 languages foot_0 that exist in the world @xcite . the unsupervised morphological paradigm completion task @xcite aims at generating inflections -more specifically all inflected forms, i.e., the entire paradigms, of given lemmas -without any explicit morphological information during training. a system that is able to solve this problem can generate morphological resources for most of the world's languages easily. this motivates us to participate in the sigmorphon 2020 shared task on unsupervised morphological paradigm completion @xcite . the task, however, is challenging: as the number of inflected forms per lemma is unknown a priori, an unsupervised morphological paradigm completion system needs to detect the paradigm size from raw text. since the names of morphological features expressed in a language are not known if there is no supervision, a system should mark which inflections correspond to the same morphological features across lemmas, but needs to do so without using names, cf. figure 1 . for the shared task, no external resources such as pretrained models, annotated data, or even additional monolingual text can be used. the same holds true for multilingual models. we submit two systems, which are both modifications of the official shared task baseline. the latter is a pipeline system, which performs four steps: edit tree retrieval, additional lemma retrieval, paradigm size discovery, and inflection generation @xcite . we experiment with substituting the original generation component, which is either a simple non-neural system @xcite or a transducer-based hard-attention model @xcite with an lstm encoder-decoder architecture with attention (bahdanau et al., 2015) -ims-cub1-and a pointergenerator network @xcite -ims-cub2. ims-cub2 achieves the best results of all submitted systems, outperforming the second best system by 2.07% macro-averaged best-match accuracy (bmacc; @xcite , when averaged over all languages. however, we underperform the baseline system, which performs 1.03% bmacc better than ims-cub2. looking at individual languages, ims-cub2 obtains the best results overall for bulgarian and kannada. the findings from our work on the shared task are as follows: i) the copy capabilities of a pointergenerator network are useful in this setup; and ii) unsupervised morphological paradigm completion is a challenging task: no submitted system outperforms the baselines. ## related work unsupervised methods have shown to be effective for morphological surface segmentation. lin-guistica @xcite and morfessor @xcite @xcite are two unsupervised systems for the task. in the realm of morphological generation, @xcite worked on a task which was similar to unsupervised morphological paradigm completion, but required additional knowledge (e.g., a list of morphemes). @xcite used a set of seed paradigms to train a paradigm completion model. @xcite and @xcite al. (2014) in a fully supervised way is the more common approach. methods include @xcite , @xcite , and @xcite . supervised morphological inflection has further gained popularity through previous sig-morphon and conll-sigmorphon shared tasks on the topic @xcite @xcite . the systems proposed for these shared tasks have a special relevance for our work, as we investigate the performance of morphological inflection components based on @xcite and @xcite within a pipeline for unsupervised morphological paradigm completion. ## system description in this section, we introduce our pipeline system for unsupervised morphological paradigm completion. first, we describe the baseline system, since we rely on some of its components. then, we describe our morphological inflection models. ## conclusion in this paper, we described the ims-cuboulder submission to the sigmorphon 2020 shared task on unsupervised morphological paradigm completion. we explored two modifications of the official baseline system by substituting its inflection generation component with two alternative models. thus, our final system performed 4 steps: edit tree retrieval, additional lemma retrieval, paradigm size discovery, and inflection generation. the last component was either an lstm sequence-to-sequence model with attention (ims-cub1) or a pointergenerator network (ims-cub2). although our systems could not outperform the official baselines on average, ims-cub2 was the best submitted system. it further obtained the overall highest performance for bulgarian and kannada.