# StarSpace: Embed All The Things!

arXiv: https://arxiv.org/abs/1709.03856 · HTML: https://mlsanity.com/paper/1709.03856/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Sep 2017.

## Built on by (most cited first, 24 of 24)

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- [RE-Matching: A Fine-Grained Semantic Matching Method for Zero-Shot Relation Extraction](https://mlsanity.com/paper/2306.04954/) (2023)
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- [Natural Language Understanding for Argumentative Dialogue Systems in the Opinion Building Domain](https://mlsanity.com/paper/2103.02691/) (2021)
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- [Using Person Embedding to Enrich Features and Data Augmentation for Classification](https://mlsanity.com/paper/2206.15162/) (2022)
- [Inspecting Spoken Language Understanding from Kids for Basic Math Learning at Home](https://mlsanity.com/paper/2306.00482/) (2023)
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- [Improving Dialogue Management: Quality Datasets vs Models](https://mlsanity.com/paper/2310.01339/) (2023)
- [Multi-turn Response Selection with Commonsense-enhanced Language Models](https://mlsanity.com/paper/2407.18479/) (2024)
- [NaijaNLP: A Survey of Nigerian Low-Resource Languages](https://mlsanity.com/paper/2502.19784/) (2025)
- [Learning to Detect Relevant Contexts and Knowledge for Response Selection in Retrieval-based Dialogue Systems](https://mlsanity.com/paper/2509.22845/) (2025)
- ["Stop replacing salt with sugar!'': Towards Intuitive Human-Agent Teaching](https://mlsanity.com/paper/2509.24651/) (2025)
- [Grounding Text Embeddings in Stakeholder Associations](https://mlsanity.com/paper/2605.27168/) (2026)

## Abstract (arXiv)

We present StarSpace, a general-purpose neural embedding model that can solve a wide variety of problems: labeling tasks such as text classification, ranking tasks such as information retrieval/web search, collaborative filtering-based or content-based recommendation, embedding of multi-relational graphs, and learning word, sentence or document level embeddings. In each case the model works by embedding those entities comprised of discrete features and comparing them against each other -- learning similarities dependent on the task. Empirical results on a number of tasks show that StarSpace is highly competitive with existing methods, whilst also being generally applicable to new cases where those methods are not.