# Fine-Grained Prediction of Syntactic Typology: Discovering Latent Structure with Supervised Learning

arXiv: https://arxiv.org/abs/1710.03877 · HTML: https://mlsanity.com/paper/1710.03877/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Oct 2017.

## Built on by (most cited first, 4 of 4)

- [Language Embeddings for Typology and Cross-lingual Transfer Learning](https://mlsanity.com/paper/2106.02082/) (2021)
- [AUTOLEX: An Automatic Framework for Linguistic Exploration](https://mlsanity.com/paper/2203.13901/) (2022)
- [Teacher Perception of Automatically Extracted Grammar Concepts for L2 Language Learning](https://mlsanity.com/paper/2206.05154/) (2022)
- [Teacher Perception of Automatically Extracted Grammar Concepts for L2 Language Learning](https://mlsanity.com/paper/2310.18417/) (2023)

## Abstract (arXiv)

We show how to predict the basic word-order facts of a novel language given only a corpus of part-of-speech (POS) sequences. We predict how often direct objects follow their verbs, how often adjectives follow their nouns, and in general the directionalities of all dependency relations. Such typological properties could be helpful in grammar induction. While such a problem is usually regarded as unsupervised learning, our innovation is to treat it as supervised learning, using a large collection of realistic synthetic languages as training data. The supervised learner must identify surface features of a language's POS sequence (hand-engineered or neural features) that correlate with the language's deeper structure (latent trees). In the experiment, we show: 1) Given a small set of real languages, it helps to add many synthetic languages to the training data. 2) Our system is robust even when the POS sequences include noise. 3) Our system on this task outperforms a grammar induction baseline by a large margin.