# Morph-fitting: Fine-Tuning Word Vector Spaces with Simple Language-Specific Rules

## TL;DR

This paper introduces morph-fitting, a simple language-specific rule-based method to refine word vector spaces by leveraging morphological constraints, improving low-frequency word representations and semantic quality for language understanding.

## Contribution

The paper presents a novel morph-fitting approach that uses morphological rules instead of curated lexicons to enhance word vector spaces across multiple languages.

## Key findings

- Improves low-frequency word estimates
- Enhances semantic quality of word vectors
- Boosts performance in dialogue state tracking

## Abstract

Morphologically rich languages accentuate two properties of distributional vector space models: 1) the difficulty of inducing accurate representations for low-frequency word forms; and 2) insensitivity to distinct lexical relations that have similar distributional signatures. These effects are detrimental for language understanding systems, which may infer that 'inexpensive' is a rephrasing for 'expensive' or may not associate 'acquire' with 'acquires'. In this work, we propose a novel morph-fitting procedure which moves past the use of curated semantic lexicons for improving distributional vector spaces. Instead, our method injects morphological constraints generated using simple language-specific rules, pulling inflectional forms of the same word close together and pushing derivational antonyms far apart. In intrinsic evaluation over four languages, we show that our approach: 1) improves low-frequency word estimates; and 2) boosts the semantic quality of the entire word vector collection. Finally, we show that morph-fitted vectors yield large gains in the downstream task of dialogue state tracking, highlighting the importance of morphology for tackling long-tail phenomena in language understanding tasks.