Multi-Intent Attribute-Aware Text Matching in Searching
Mingzhe Li Xiuying Chen Jing Xiang Qishen Zhang Changsheng Ma Chenchen Dai Jinxiong Chang Zhongyi Liu Guannan Zhang
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.