CoNLL-SIGMORPHON 2017 Shared Task: Universal Morphological Reinflection in 52 Languages

Abstract

The CoNLL-SIGMORPHON 2017 shared task on supervised morphological generation required systems to be trained and tested in each of 52 typologically diverse languages. In sub-task 1, submitted systems were asked to predict a specific inflected form of a given lemma. In sub-task 2, systems were given a lemma and some of its specific inflected forms, and asked to complete the inflectional paradigm by predicting all of the remaining inflected forms. Both sub-tasks included high, medium, and low-resource conditions. Sub-task 1 received 24 system submissions, while sub-task 2 received 3 system submissions. Following the success of neural sequence-to-sequence models in the SIGMORPHON 2016 shared task, all but one of the submissions included a neural component. The results show that high performance can be achieved with small training datasets, so long as models have appropriate inductive bias or make use of additional unlabeled data or synthetic data. However, different biasing and data augmentation resulted in non-identical sets of inflected forms being predicted correctly, suggesting that there is room for future improvement.

If you can't beat them, join them: the University of Alberta system description

“…In this paper, we describe our system as participants in the CoNLL-SIGMORPHON 2017 Shared Task on Universal Morphological Reinflection (Cotterell et al, 2017). Our focus is on the sub-task of inflection generation under the lowresource scenario, in which the training data is limited to 100 labeled examples, with and without monolingual corpora.…”

SU-RUG at the CoNLL-SIGMORPHON 2017 shared task: Morphological Inflection with Attentional Sequence-to-Sequence Models

“…We focus on task 1 of the SIGMORPHON 2017 shared task (Cotterell et al, 2017), morphological inflection. The task is to learn the mapping from a lemma and morphological description to the corresponding inflected form.…”

Character Sequence-to-Sequence Model with Global Attention for Universal Morphological Reinflection

“…Within the paradigm, inflected word forms of lexemes are defined by the requirements of syntactic rules. A word's form reflects syntactic and semantic features that are expressed by the word, such as the conjugations of verbs, and the declensions of nouns (Cotterell et al, 2017). For example, every English count noun has both singular and plural forms, known as the inflected forms of the noun.…”

“…For the CoNLL-SIGMORPHON-2017 Shared Task 1 (Cotterell et al, 2017) on morphological reinflection, given a lemma (the dictionary form of a word) and target morphosyntactic descriptions, a target inflected form is required to be generated across 52 different languages. In each of these languages, there are three training sets (high, medium, and low) representing different amount of training data (Scottish Gaelic only has medium and low).…”