---
title: "Improving Vector Space Word Representations — Faruqui 2014"
description: "Faruqui and Dyer test whether word vectors trained on a single language improve when they also use information about how words translate."
authors: ["Manaal Faruqui", "Chris Dyer"]
year: 2014
venue: "Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics"
source: "https://aclanthology.org/E14-1049/"
doi: "10.3115/v1/e14-1049"
licence: "CC BY-NC-SA 3.0 (ACL Anthology, before 2016)"
licence_url: "https://creativecommons.org/licenses/by-nc-sa/3.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/faruqui-2014-improving-vector-space-word-representations/"
---
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# Improving Vector Space Word Representations Using Multilingual Correlation

**Authors:** [Manaal Faruqui](https://md.research.elamur.ai/free/en/authors/manaal-faruqui/), [Chris Dyer](https://md.research.elamur.ai/free/en/authors/chris-dyer/) · **Year:** 2014 · **Venue:** Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics · **Licence:** [CC BY-NC-SA 3.0 (ACL Anthology, before 2016)](https://creativecommons.org/licenses/by-nc-sa/3.0/)

**Links:** [Publisher's page](https://aclanthology.org/E14-1049/) · [PDF](https://aclanthology.org/E14-1049.pdf) · [DOI 10.3115/v1/e14-1049](https://doi.org/10.3115/v1/e14-1049) · [OpenAlex](https://openalex.org/W342285082)

**Citations:** 618 citations (openalex-snapshot · 2026-10-14) · [201](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
Faruqui and Dyer test whether word vectors trained on a single language improve when they also use information about how words translate. Their premise is that word meaning should stay stable across languages. If several English words share one translation in another language, that suggests they are near-synonyms. The method has two steps. First, separate vector space models are trained for each language. Then canonical correlation analysis (CCA) is run on translation pairs taken from automatic word alignments. CCA finds projections under which paired vectors are as correlated as possible. These projections are then applied to the whole vocabulary, so words with no translation also get new vectors. Only the most correlated dimensions are kept. The authors start with English LSA vectors paired with German, French and Spanish. They then try RNN language-model vectors and skip-gram vectors. Evaluation covers word similarity benchmarks and analogy tasks. LSA and RNN vectors improve clearly, while skip-gram results are mixed. The work suggests that bilingual data can help even applications that use only one language.

## Key points
- Base model: PMI-weighted word-word co-occurrence matrix factorized with SVD, giving 640-dimensional LSA vectors for English, German, French and Spanish. English is projected with CCA against each of the other three languages.
- The share of top correlated dimensions kept (k) was tuned only on WS-353. The best average across language pairs was k = 80%, and this value was reused on all other benchmarks.
- Benchmarks:
  - word similarity: WS-353 (plus its WS-SIM and WS-REL splits), RG-65, MC-30 and MTurk-287, scored with Spearman correlation of cosine similarities;
  - analogies: SEM-REL and SYN-REL from Mikolov et al., solved with the vector offset method.
- With 640-dimensional LSA, the paper reports absolute gains of at least 20 points over the monolingual baseline. Results were nearly the same for all three language pairs.
- Results with 80-dimensional monolingual vectors versus 48-dimensional multilingual ones:

  | Vectors | WS-353 | SYN-REL |
  |---|---|---|
  | SVD | 34.8 → 58.1 | 24.4 → 33.2 |
  | RNN (RNNLM toolkit) | 23.6 → 35.4 | 18.2 → 12.2 |
  | Skip-gram (word2vec) | 63.9 → 63.1 | not given here |

  - RNN vectors improved on similarity but dropped on analogies.
  - Skip-gram changed little overall.

## Interesting ideas
- **Translation as a signal of meaning:** Treating "shares a translation" as evidence of synonymy is a cheap way to sharpen word vectors. For Ukrainian, parallel text with English or Polish could be used this way without any manual annotation.
- **Learn the projection on aligned pairs, apply it to everything:** CCA is fit only on words that have a one-to-one translation. The resulting linear maps then transform the entire vocabulary. This avoids the out-of-vocabulary problem that simply concatenating vectors from both languages would create.
- **Keep only the top-k correlated dimensions:** CCA outputs its directions already sorted by correlation, so truncating them gives compression and denoising in one step. Treating k as a percentage makes it easy to tune.
- **Tune once, then reuse:** The hyperparameter was chosen on one benchmark using the average over language pairs, then fixed for all other tasks. This is a simple guard against overfitting each evaluation separately.
- **Checking which kind of information transfers:** Results were stable across language pairs for count-based and skip-gram vectors but varied for RNN vectors. The authors read this as evidence that semantic information carries over between languages better than syntactic information. The same comparison could help decide where cross-lingual projection is worth applying.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/)

## Cited by

Cited by

201 papers of our library cite it (of the 140884 whose bibliographies we read); 17 of them have a page here, 184 not yet.

- [Neural Decipherment via Minimum-Cost Flow: From Ugaritic to Linear B](https://md.research.elamur.ai/free/en/papers/acl-2019-luo-neural-decipherment-minimum-cost-flow/)
- [Leveraging Pretrained Word Embeddings for Part-of-Speech Tagging of Code Switching Data](https://md.research.elamur.ai/free/en/papers/alghamdi-2019-leveraging-pretrained-word-embeddings-part/)
- [Massively Multilingual Word Embeddings](https://md.research.elamur.ai/free/en/papers/ammar-2016-massively-multilingual-word-embeddings/)
- [Learning bilingual word embeddings with (almost) no bilingual data](https://md.research.elamur.ai/free/en/papers/artetxe-2017-learning-bilingual-word-embeddings-almost/)
- [Problems With Evaluation of Word Embeddings Using Word Similarity Tasks](https://md.research.elamur.ai/free/en/papers/faruqui-2016-problems-evaluation-word-embeddings-word/)
- [Cross-lingual Dependency Parsing Based on Distributed Representations](https://md.research.elamur.ai/free/en/papers/guo-2015-cross-lingual-dependency-parsing-based/)
- [XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization](https://md.research.elamur.ai/free/en/papers/hu-2020-xtreme-massively-multilingual-multi-task/)
- [Neural Cross-Lingual Coreference Resolution And Its Application To Entity Linking](https://md.research.elamur.ai/free/en/papers/kundu-2018-neural-cross-lingual-coreference-resolution/)
- [Visualizing and Understanding Neural Models in NLP](https://md.research.elamur.ai/free/en/papers/li-2016-visualizing-understanding-neural-models-nlp/)
- [Two/Too Simple Adaptations of Word2Vec for Syntax Problems](https://md.research.elamur.ai/free/en/papers/ling-2015-two-too-simple-adaptations-word2vec/)
- [URIEL and lang2vec: Representing languages as typological, geographical, and phylogenetic vectors](https://md.research.elamur.ai/free/en/papers/littell-2017-uriel-lang2vec-representing-languages-as/)
- [Bilingual Word Representations with Monolingual Quality in Mind](https://md.research.elamur.ai/free/en/papers/luong-2015-bilingual-word-representations-monolingual-quality/)
- [Pre-trained Models for Natural Language Processing: A Survey](https://md.research.elamur.ai/free/en/papers/qiu-2020-pre-trained-models-natural-language/)
- [Offline bilingual word vectors, orthogonal transformations and the inverted softmax](https://md.research.elamur.ai/free/en/papers/smith-2017-offline-bilingual-word-vectors-orthogonal/)
- [Metaphor Detection with Cross-Lingual Model Transfer](https://md.research.elamur.ai/free/en/papers/tsvetkov-2014-metaphor-detection-cross-lingual-model/)
- [Normalized Word Embedding and Orthogonal Transform for Bilingual Word Translation](https://md.research.elamur.ai/free/en/papers/xing-2015-normalized-word-embedding-orthogonal-transform/)
- [Adversarial Training for Unsupervised Bilingual Lexicon Induction](https://md.research.elamur.ai/free/en/papers/zhang-2017-adversarial-training-unsupervised-bilingual-lexicon/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{faruqui2014improving,
  title = {{Improving Vector Space Word Representations Using Multilingual Correlation}},
  author = {Faruqui, Manaal and Dyer, Chris},
  booktitle = {Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics},
  year = {2014},
  doi = {10.3115/v1/e14-1049},
  url = {https://doi.org/10.3115/v1/e14-1049}
}
```

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