(Un)solving Morphological Inflection: Lemma Overlap Artificially Inflates Models' Performance

• URL: http://arxiv.org/abs/2108.05682v1
• Date: Thu, 12 Aug 2021 12:06:47 GMT
• Title: (Un)solving Morphological Inflection: Lemma Overlap Artificially Inflates Models' Performance
• Abstract summary: We propose to re-evaluate morphological inflection models by employing harder train-test splits. Experiments with the three top-ranked systems on the SIGMORPHON's 2020 shared-task show that the lemma-split presents an average drop of 30 percentage points.
• Score: 7.2223831790495625
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In the domain of Morphology, Inflection is a fundamental and important task that gained a lot of traction in recent years, mostly via SIGMORPHON's shared-tasks. With average accuracy above 0.9 over the scores of all languages, the task is considered mostly solved using relatively generic neural sequence-to-sequence models, even with little data provided. In this work, we propose to re-evaluate morphological inflection models by employing harder train-test splits that will challenge the generalization capacity of the models. In particular, as opposed to the na\"ive split-by-form, we propose a split-by-lemma method to challenge the performance on existing benchmarks. Our experiments with the three top-ranked systems on the SIGMORPHON's 2020 shared-task show that the lemma-split presents an average drop of 30 percentage points in macro-average for the 90 languages included. The effect is most significant for low-resourced languages with a drop as high as 95 points, but even high-resourced languages lose about 10 points on average. Our results clearly show that generalizing inflection to unseen lemmas is far from being solved, presenting a simple yet effective means to promote more sophisticated models.

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