Context Enhanced Short Text Matching using Clickthrough Data
This addresses the issue of incomplete semantics in short text matching for applications like search and recommendation, though it appears incremental.
The paper tackled the problem of short text matching by introducing external knowledge to enhance contextual representation, resulting in a framework that outperformed state-of-the-art models on two Chinese and one English dataset.
The short text matching task employs a model to determine whether two short texts have the same semantic meaning or intent. Existing short text matching models usually rely on the content of short texts which are lack information or missing some key clues. Therefore, the short texts need external knowledge to complete their semantic meaning. To address this issue, we propose a new short text matching framework for introducing external knowledge to enhance the short text contextual representation. In detail, we apply a self-attention mechanism to enrich short text representation with external contexts. Experiments on two Chinese datasets and one English dataset demonstrate that our framework outperforms the state-of-the-art short text matching models.