IART: Intent-aware Response Ranking with Transformers in Information-seeking Conversation Systems

Understand

Personal assistant systems, such as Apple Siri, Google Assistant, Amazon Alexa, and Microsoft Cortana, are becoming ever more widely used.

• Understanding user intent such as clarification questions, potential answers and user feedback in information-seeking conversations is critical for retrieving good responses.
• In this paper, we analyze user intent patterns in information-seeking conversations and propose an intent-aware neural response ranking model "IART", which refers to "Intent-Aware Ranking with Transformers".
• IART is built on top of the integration of user intent modeling and language representation learning with the Transformer architecture, which relies entirely on a self-attention mechanism instead of recurrent nets.