# Minimal Supervision for Morphological Inflection

arXiv: https://arxiv.org/abs/2104.08512 · HTML: https://mlsanity.com/paper/2104.08512/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 41 references that are in the map.
- First posted on arXiv 17 Apr 2021.

## Builds on (most cited first, 14 of 14)

- [Word Translation Without Parallel Data](https://mlsanity.com/paper/1710.04087/) (2017)
- [Learning Word Vectors for 157 Languages](https://mlsanity.com/paper/1802.06893/) (2018)
- [The SIGMORPHON 2019 Shared Task: Morphological Analysis in Context and Cross-Lingual Transfer for Inflection](https://mlsanity.com/paper/1910.11493/) (2019)
- [The CoNLL--SIGMORPHON 2018 Shared Task: Universal Morphological Reinflection](https://mlsanity.com/paper/1810.07125/) (2018)
- [CoNLL-SIGMORPHON 2017 Shared Task: Universal Morphological Reinflection in 52 Languages](https://mlsanity.com/paper/1706.09031/) (2017)
- [Pushing the Limits of Low-Resource Morphological Inflection](https://mlsanity.com/paper/1908.05838/) (2019)
- [Morphosyntactic Tagging with a Meta-BiLSTM Model over Context Sensitive Token Encodings](https://mlsanity.com/paper/1805.08237/) (2018)
- [Don't Forget the Long Tail! A Comprehensive Analysis of Morphological Generalization in Bilingual Lexicon Induction](https://mlsanity.com/paper/1909.02855/) (2019)
- [Morphological Inflection Generation Using Character Sequence to Sequence Learning](https://mlsanity.com/paper/1512.06110/) (2015)
- [An Unsupervised Method for Uncovering Morphological Chains](https://mlsanity.com/paper/1503.02335/) (2015)
- [Unsupervised Morphological Paradigm Completion](https://mlsanity.com/paper/2005.00970/) (2020)
- [The Paradigm Discovery Problem](https://mlsanity.com/paper/2005.01630/) (2020)
- [One-Shot Neural Cross-Lingual Transfer for Paradigm Completion](https://mlsanity.com/paper/1704.00052/) (2017)
- [Learning to Learn Morphological Inflection for Resource-Poor Languages](https://mlsanity.com/paper/2004.13304/) (2020)

## Built on by (most cited first, 1 of 1)

- [(Un)solving Morphological Inflection: Lemma Overlap Artificially Inflates Models' Performance](https://mlsanity.com/paper/2108.05682/) (2021)

## Abstract (arXiv)

Neural models for the various flavours of morphological inflection tasks have proven to be extremely accurate given ample labeled data -- data that may be slow and costly to obtain. In this work we aim to overcome this annotation bottleneck by bootstrapping labeled data from a seed as little as em five labeled paradigms, accompanied by a large bulk of unlabeled text. Our approach exploits different kinds of regularities in morphological systems in a two-phased setup, where word tagging based on em analogies is followed by word pairing based on em distances. We experiment with the Paradigm Cell Filling Problem over eight typologically different languages, and find that, in languages with relatively simple morphology, orthographic regularities on their own allow inflection models to achieve respectable accuracy. Combined orthographic and semantic regularities alleviate difficulties with particularly complex morpho-phonological systems. Our results suggest that hand-crafting many tagged examples might be an unnecessary effort. However, more work is needed in order to address rarely used forms.