  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. It
incorporates intent-aware utterance attention to derive an importance weighting
scheme of utterances in conversation context with the aim of better
conversation history understanding. We conduct extensive experiments with three
information-seeking conversation data sets including both standard benchmarks
and commercial data. Our proposed model outperforms all baseline methods with
respect to a variety of metrics. We also perform case studies and analysis of
learned user intent and its impact on response ranking in information-seeking
conversations to provide interpretation of results.
