# Multi-word Term Embeddings Improve Lexical Product Retrieval

arXiv: https://arxiv.org/abs/2406.01233 · HTML: https://mlsanity.com/paper/2406.01233/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 64 references that are in the map.
- First posted on arXiv 3 Jun 2024.

## Builds on (most cited first, 25 of 25)

- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [Character-level Convolutional Networks for Text Classification](https://mlsanity.com/paper/1509.01626/) (2015)
- [SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing](https://mlsanity.com/paper/1808.06226/) (2018)
- [ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT](https://mlsanity.com/paper/2004.12832/) (2020)
- [Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval](https://mlsanity.com/paper/2007.00808/) (2020)
- [Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates](https://mlsanity.com/paper/1804.10959/) (2018)
- [Document Expansion by Query Prediction](https://mlsanity.com/paper/1904.08375/) (2019)
- [Efficient Natural Language Response Suggestion for Smart Reply](https://mlsanity.com/paper/1705.00652/) (2017)
- [Multilingual Universal Sentence Encoder for Semantic Retrieval](https://mlsanity.com/paper/1907.04307/) (2019)
- [Pre-training Tasks for Embedding-based Large-scale Retrieval](https://mlsanity.com/paper/2002.03932/) (2020)
- [Improving Efficient Neural Ranking Models with Cross-Architecture Knowledge Distillation](https://mlsanity.com/paper/2010.02666/) (2020)
- [Character-Aware Neural Language Models](https://mlsanity.com/paper/1508.06615/) (2015)
- [On Using Very Large Target Vocabulary for Neural Machine Translation](https://mlsanity.com/paper/1412.2007/) (2014)
- [Context-Aware Sentence/Passage Term Importance Estimation For First Stage Retrieval](https://mlsanity.com/paper/1910.10687/) (2019)
- [Distilling Dense Representations for Ranking using Tightly-Coupled Teachers](https://mlsanity.com/paper/2010.11386/) (2020)
- [Semantic Product Search](https://mlsanity.com/paper/1907.00937/) (2019)
- [Embedding-based Product Retrieval in Taobao Search](https://mlsanity.com/paper/2106.09297/) (2021)
- [End-to-End Retrieval in Continuous Space](https://mlsanity.com/paper/1811.08008/) (2018)
- [SparTerm: Learning Term-based Sparse Representation for Fast Text Retrieval](https://mlsanity.com/paper/2010.00768/) (2020)
- [Complementing Lexical Retrieval with Semantic Residual Embedding](https://mlsanity.com/paper/2004.13969/) (2020)
- [TwinBERT: Distilling Knowledge to Twin-Structured BERT Models for Efficient Retrieval](https://mlsanity.com/paper/2002.06275/) (2020)
- [Off the Beaten Path: Let's Replace Term-Based Retrieval with k-NN Search](https://mlsanity.com/paper/1610.10001/) (2016)

## Abstract (arXiv)

Product search is uniquely different from search for documents, Internet resources or vacancies, therefore it requires the development of specialized search systems. The present work describes the H1 embdedding model, designed for an offline term indexing of product descriptions at e-commerce platforms. The model is compared to other state-of-the-art (SoTA) embedding models within a framework of hybrid product search system that incorporates the advantages of lexical methods for product retrieval and semantic embedding-based methods. We propose an approach to building semantically rich term vocabularies for search indexes. Compared to other production semantic models, H1 paired with the proposed approach stands out due to its ability to process multi-word product terms as one token. As an example, for search queries "new balance shoes", "gloria jeans kids wear" brand entity will be represented as one token - "new balance", "gloria jeans". This results in an increased precision of the system without affecting the recall. The hybrid search system with proposed model scores mAP@12 = 56.1% and R@1k = 86.6% on the WANDS public dataset, beating other SoTA analogues.