# Don't Forget the Long Tail! A Comprehensive Analysis of Morphological Generalization in Bilingual Lexicon Induction

arXiv: https://arxiv.org/abs/1909.02855 · HTML: https://mlsanity.com/paper/1909.02855/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Sep 2019.

## Built on by (most cited first, 15 of 15)

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- [Bilingual Lexicon Induction for Low-Resource Languages using Graph Matching via Optimal Transport](https://mlsanity.com/paper/2210.14378/) (2022)
- [Sentence Alignment with Parallel Documents Facilitates Biomedical Machine Translation](https://mlsanity.com/paper/2104.08588/) (2021)
- [Is Probing All You Need? Indicator Tasks as an Alternative to Probing Embedding Spaces](https://mlsanity.com/paper/2310.15905/) (2023)
- [Minimal Supervision for Morphological Inflection](https://mlsanity.com/paper/2104.08512/) (2021)
- [Constrained Density Matching and Modeling for Cross-lingual Alignment of Contextualized Representations](https://mlsanity.com/paper/2201.13429/) (2022)
- [Out-of-Distribution Generalization in Text Classification: Past, Present, and Future](https://mlsanity.com/paper/2305.14104/) (2023)
- [On Using Distribution-Based Compositionality Assessment to Evaluate Compositional Generalisation in Machine Translation](https://mlsanity.com/paper/2311.08249/) (2023)
- [BERT Cannot Align Characters](https://mlsanity.com/paper/2109.09700/) (2021)

## Abstract (arXiv)

Human translators routinely have to translate rare inflections of words - due to the Zipfian distribution of words in a language. When translating from Spanish, a good translator would have no problem identifying the proper translation of a statistically rare inflection such as habl'aramos. Note the lexeme itself, hablar, is relatively common. In this work, we investigate whether state-of-the-art bilingual lexicon inducers are capable of learning this kind of generalization. We introduce 40 morphologically complete dictionaries in 10 languages and evaluate three of the state-of-the-art models on the task of translation of less frequent morphological forms. We demonstrate that the performance of state-of-the-art models drops considerably when evaluated on infrequent morphological inflections and then show that adding a simple morphological constraint at training time improves the performance, proving that the bilingual lexicon inducers can benefit from better encoding of morphology.