# GRI: Graph-based Relative Isomorphism of Word Embedding Spaces

arXiv: https://arxiv.org/abs/2310.12360 · HTML: https://mlsanity.com/paper/2310.12360/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 30 references that are in the map.
- First posted on arXiv 18 Oct 2023.

## Builds on (most cited first, 24 of 24)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Cross-lingual Language Model Pretraining](https://mlsanity.com/paper/1901.07291/) (2019)
- [Word Translation Without Parallel Data](https://mlsanity.com/paper/1710.04087/) (2017)
- [Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond](https://mlsanity.com/paper/1812.10464/) (2018)
- [Exploiting Similarities among Languages for Machine Translation](https://mlsanity.com/paper/1309.4168/) (2013)
- [A robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings](https://mlsanity.com/paper/1805.06297/) (2018)
- [Phrase-Based & Neural Unsupervised Machine Translation](https://mlsanity.com/paper/1804.07755/) (2018)
- [Loss in Translation: Learning Bilingual Word Mapping with a Retrieval Criterion](https://mlsanity.com/paper/1804.07745/) (2018)
- [Cross-Lingual Alignment of Contextual Word Embeddings, with Applications to Zero-shot Dependency Parsing](https://mlsanity.com/paper/1902.09492/) (2019)
- [On the Limitations of Unsupervised Bilingual Dictionary Induction](https://mlsanity.com/paper/1805.03620/) (2018)
- [How to (Properly) Evaluate Cross-Lingual Word Embeddings: On Strong Baselines, Comparative Analyses, and Some Misconceptions](https://mlsanity.com/paper/1902.00508/) (2019)
- [When Does Unsupervised Machine Translation Work?](https://mlsanity.com/paper/2004.05516/) (2020)
- [Bilingual Lexicon Induction with Semi-supervision in Non-Isometric Embedding Spaces](https://mlsanity.com/paper/1908.06625/) (2019)
- [When and Why is Unsupervised Neural Machine Translation Useless?](https://mlsanity.com/paper/2004.10581/) (2020)
- [Massively Multilingual Word Embeddings](https://mlsanity.com/paper/1602.01925/) (2016)
- [Learning Multilingual Word Embeddings in Latent Metric Space: A Geometric Approach](https://mlsanity.com/paper/1808.08773/) (2018)
- [Context-Aware Cross-Lingual Mapping](https://mlsanity.com/paper/1903.03243/) (2019)
- [BilBOWA: Fast Bilingual Distributed Representations without Word Alignments](https://mlsanity.com/paper/1410.2455/) (2014)
- [Are Girls Neko or Sh=ojo? Cross-Lingual Alignment of Non-Isomorphic Embeddings with Iterative Normalization](https://mlsanity.com/paper/1906.01622/) (2019)
- [Density Matching for Bilingual Word Embedding](https://mlsanity.com/paper/1904.02343/) (2019)
- [Antonym-Synonym Classification Based on New Sub-space Embeddings](https://mlsanity.com/paper/1906.05612/) (2019)
- [IsoVec: Controlling the Relative Isomorphism of Word Embedding Spaces](https://mlsanity.com/paper/2210.05098/) (2022)

## Built on by (most cited first, 3 of 3)

- [Prompt-SAW: Leveraging Relation-Aware Graphs for Textual Prompt Compression](https://mlsanity.com/paper/2404.00489/) (2024)
- [MQA-KEAL: Multi-hop Question Answering under Knowledge Editing for Arabic Language](https://mlsanity.com/paper/2409.12257/) (2024)
- [Antonym vs Synonym Distinction using InterlaCed Encoder NETworks (ICE-NET)](https://mlsanity.com/paper/2401.10045/) (2024)

## Abstract (arXiv)

Automated construction of bilingual dictionaries using monolingual embedding spaces is a core challenge in machine translation. The end performance of these dictionaries relies upon the geometric similarity of individual spaces, i.e., their degree of isomorphism. Existing attempts aimed at controlling the relative isomorphism of different spaces fail to incorporate the impact of semantically related words in the training objective. To address this, we propose GRI that combines the distributional training objectives with attentive graph convolutions to unanimously consider the impact of semantically similar words required to define/compute the relative isomorphism of multiple spaces. Experimental evaluation shows that GRI outperforms the existing research by improving the average P@1 by a relative score of up to 63.6%. We release the codes for GRI at https://github.com/asif6827/GRI.