A Deep Architecture for Semantic Matching with Multiple Positional Sentence Representations

Understand

Matching natural language sentences is central for many applications such as information retrieval and question answering.

• Existing deep models rely on a single sentence representation or multiple granularity representations for matching.
• However, such methods cannot well capture the contextualized local information in the matching process.
• To tackle this problem, we present a new deep architecture to match two sentences with multiple positional sentence representations.