Multi-Intent Attribute-Aware Text Matching in Searching

• URL: http://arxiv.org/abs/2402.07788v1
• Date: Mon, 12 Feb 2024 16:54:22 GMT
• Title: Multi-Intent Attribute-Aware Text Matching in Searching
• Authors: Mingzhe Li, Xiuying Chen, Jing Xiang, Qishen Zhang, Changsheng Ma, Chenchen Dai, Jinxiong Chang, Zhongyi Liu, Guannan Zhang
• Abstract summary: 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 MIM, the text and attributes are weighted and processed through a scaled attention mechanism with regard to the attributes' importance. In the intent-aware matching, the intents are evaluated by a self-supervised masking task, and then incorporated to output the final matching result.
• Score: 21.92265431319774
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• QUIDS: Query Intent Generation via Dual Space Modeling [12.572815037915348]
  We propose a dual-space model that uses semantic relevance and irrelevance information in the returned documents to explain the understanding of the query intent. Our methods produce high-quality query intent descriptions, outperforming existing methods for this task, as well as state-of-the-art query-based summarization methods.
  arXiv  Detail & Related papers  (2024-10-16T09:28:58Z)
• MARS: Matching Attribute-aware Representations for Text-based Sequential Recommendation [11.460164505052981]
  We propose a novel model, Matching Attribute-aware Representations for Text-based Sequential Recommendation (MARS) MARS extracts detailed user and item representations through attribute-aware text encoding, capturing diverse user intents with multiple attribute-aware representations. It then computes user-item scores via attribute-wise interaction matching, effectively capturing attribute-level user preferences.
  arXiv  Detail & Related papers  (2024-09-01T12:11:48Z)
• EAVE: Efficient Product Attribute Value Extraction via Lightweight Sparse-layer Interaction [94.22610101608332]
  We propose an Efficient product Attribute Value Extraction (EAVE) approach via lightweight sparse-layer interaction. We employ a heavy encoder to separately encode the product context and attribute. The resulting non-interacting heavy representations of the context can be cached and reused for all attributes. Our method achieves significant efficiency gains with neutral or marginal loss in performance when the context is long and number of attributes is large.
  arXiv  Detail & Related papers  (2024-06-10T23:06:38Z)
• Answering Compositional Queries with Set-Theoretic Embeddings [43.926610595182126]
  Box embeddings are a region-based representation that can be thought of as learnable Venn diagrams. We present experiments and analysis providing insights into the behavior of both. We find that, while vector and box embeddings are equally suited to single attribute queries, for compositional queries box embeddings provide substantial advantages.
  arXiv  Detail & Related papers  (2023-06-07T04:04:36Z)
• FashionSearchNet-v2: Learning Attribute Representations with Localization for Image Retrieval with Attribute Manipulation [22.691709684780292]
  The proposed FashionSearchNet-v2 architecture is able to learn attribute specific representations by leveraging on its weakly-supervised localization module. The network is jointly trained with the combination of attribute classification and triplet ranking loss to estimate local representations. Experiments performed on several datasets that are rich in terms of the number of attributes show that FashionSearchNet-v2 outperforms the other state-of-the-art attribute manipulation techniques.
  arXiv  Detail & Related papers  (2021-11-28T13:50:20Z)
• AdaTag: Multi-Attribute Value Extraction from Product Profiles with Adaptive Decoding [55.89773725577615]
  We present AdaTag, which uses adaptive decoding to handle attribute extraction. Our experiments on a real-world e-Commerce dataset show marked improvements over previous methods.
  arXiv  Detail & Related papers  (2021-06-04T07:54:11Z)
• Fine-Grained Fashion Similarity Prediction by Attribute-Specific Embedding Learning [71.74073012364326]
  We propose an Attribute-Specific Embedding Network (ASEN) to jointly learn multiple attribute-specific embeddings. The proposed ASEN is comprised of a global branch and a local branch. Experiments on three fashion-related datasets, i.e., FashionAI, DARN, and DeepFashion, show the effectiveness of ASEN for fine-grained fashion similarity prediction.
  arXiv  Detail & Related papers  (2021-04-06T11:26:38Z)
• Dynamic Semantic Matching and Aggregation Network for Few-shot Intent Detection [69.2370349274216]
  Few-shot Intent Detection is challenging due to the scarcity of available annotated utterances. Semantic components are distilled from utterances via multi-head self-attention. Our method provides a comprehensive matching measure to enhance representations of both labeled and unlabeled instances.
  arXiv  Detail & Related papers  (2020-10-06T05:16:38Z)
• Automatic Validation of Textual Attribute Values in E-commerce Catalog by Learning with Limited Labeled Data [61.789797281676606]
  We propose a novel meta-learning latent variable approach, called MetaBridge. It can learn transferable knowledge from a subset of categories with limited labeled data. It can capture the uncertainty of never-seen categories with unlabeled data.
  arXiv  Detail & Related papers  (2020-06-15T21:31:05Z)
• Joint Item Recommendation and Attribute Inference: An Adaptive Graph Convolutional Network Approach [61.2786065744784]
  In recommender systems, users and items are associated with attributes, and users show preferences to items. As annotating user (item) attributes is a labor intensive task, the attribute values are often incomplete with many missing attribute values. We propose an Adaptive Graph Convolutional Network (AGCN) approach for joint item recommendation and attribute inference.
  arXiv  Detail & Related papers  (2020-05-25T10:50:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.