# Multi-Intent Attribute-Aware Text Matching in Searching

arXiv: https://arxiv.org/abs/2402.07788 · HTML: https://mlsanity.com/paper/2402.07788/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 41 references that are in the map.
- First posted on arXiv 12 Feb 2024.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://mlsanity.com/paper/2104.08821/) (2021)
- [ConSERT: A Contrastive Framework for Self-Supervised Sentence Representation Transfer](https://mlsanity.com/paper/2105.11741/) (2021)
- [Augmented SBERT: Data Augmentation Method for Improving Bi-Encoders for Pairwise Sentence Scoring Tasks](https://mlsanity.com/paper/2010.08240/) (2020)
- [CGMH: Constrained Sentence Generation by Metropolis-Hastings Sampling](https://mlsanity.com/paper/1811.10996/) (2018)
- [Controllable Multi-Interest Framework for Recommendation](https://mlsanity.com/paper/2005.09347/) (2020)
- [VMSMO: Learning to Generate Multimodal Summary for Video-based News Articles](https://mlsanity.com/paper/2010.05406/) (2020)
- [Multi-View Document Representation Learning for Open-Domain Dense Retrieval](https://mlsanity.com/paper/2203.08372/) (2022)
- [Let GPT be a Math Tutor: Teaching Math Word Problem Solvers with Customized Exercise Generation](https://mlsanity.com/paper/2305.14386/) (2023)
- [StruBERT: Structure-aware BERT for Table Search and Matching](https://mlsanity.com/paper/2203.14278/) (2022)
- [RankCSE: Unsupervised Sentence Representations Learning via Learning to Rank](https://mlsanity.com/paper/2305.16726/) (2023)
- [CAKE: A Scalable Commonsense-Aware Framework For Multi-View Knowledge Graph Completion](https://mlsanity.com/paper/2202.13785/) (2022)
- [Query Rewriting via Cycle-Consistent Translation for E-Commerce Search](https://mlsanity.com/paper/2103.00800/) (2021)
- [Modeling User Behavior with Graph Convolution for Personalized Product Search](https://mlsanity.com/paper/2202.06081/) (2022)
- [Divide and Conquer: Text Semantic Matching with Disentangled Keywords and Intents](https://mlsanity.com/paper/2203.02898/) (2022)
- [Keywords and Instances: A Hierarchical Contrastive Learning Framework Unifying Hybrid Granularities for Text Generation](https://mlsanity.com/paper/2205.13346/) (2022)
- [Machop: an End-to-End Generalized Entity Matching Framework](https://mlsanity.com/paper/2206.04853/) (2022)
- [A Topic-aware Summarization Framework with Different Modal Side Information](https://mlsanity.com/paper/2305.11503/) (2023)
- [RPM-Oriented Query Rewriting Framework for E-commerce Keyword-Based Sponsored Search](https://mlsanity.com/paper/1910.12527/) (2019)
- [Neural Search: Learning Query and Product Representations in Fashion E-commerce](https://mlsanity.com/paper/2107.08291/) (2021)

## Built on by (most cited first, 4 of 4)

- [Flipping Knowledge Distillation: Leveraging Small Models' Expertise to Enhance LLMs in Text Matching](https://mlsanity.com/paper/2507.05617/) (2025)
- [Pastiche Novel Generation Creating: Fan Fiction You Love in Your Favorite Author's Style](https://mlsanity.com/paper/2502.15616/) (2025)
- [One Model, Multiple Goals: Adaptive Multi-Objective Learning for E-commerce Dialogue Systems](https://mlsanity.com/paper/2606.09293/) (2026)
- [Selecting Query-bag as Pseudo Relevance Feedback for Information-seeking Conversations](https://mlsanity.com/paper/2404.04272/) (2024)

## Abstract (arXiv)

Text matching systems have become a fundamental service in most searching platforms. For instance, they are responsible for matching user queries to relevant candidate items, or rewriting the user-input query to a pre-selected high-performing one for a better search experience. In practice, both the queries and items often contain multiple attributes, such as the category of the item and the location mentioned in the query, which represent condensed key information that is helpful for matching. However, most of the existing works downplay the effectiveness of attributes by integrating them into text representations as supplementary information. Hence, in this work, we focus on exploring the relationship between the attributes from two sides. Since attributes from two ends are often not aligned in terms of number and type, we propose to exploit the benefit of attributes by multiple-intent modeling. The intents extracted from attributes summarize the diverse needs of queries and provide rich content of items, which are more refined and abstract, and can be aligned for paired inputs. Concretely, we propose a multi-intent attribute-aware matching model (MIM), which consists of three main components: attribute-aware encoder, multi-intent modeling, and intent-aware matching. In the attribute-aware encoder, the text and attributes are weighted and processed through a scaled attention mechanism with regard to the attributes' importance. Afterward, the multi-intent modeling extracts intents from two ends and aligns them. Herein, we come up with a distribution loss to ensure the learned intents are diverse but concentrated, and a kullback-leibler divergence loss that aligns the learned intents. Finally, in the intent-aware matching, the intents are evaluated by a self-supervised masking task, and then incorporated to output the final matching result.