Deciding whether to ask clarifying questions in large-scale spoken language understanding
2021 IEEE Automatic Speech Recognition and Understanding Workshop …, 2021•ieeexplore.ieee.org
A large-scale conversational agent can suffer from understanding user utterances with
various ambiguities such as ASR ambiguity, intent ambiguity, and hypothesis ambiguity.
When ambiguities are detected, the agent should engage in a clarifying dialog to resolve the
ambiguities before committing to actions. However, asking clarifying questions for all the
ambiguity occurrences could lead to asking too many questions, essentially hampering the
user experience. To trigger clarifying questions only when necessary for the user …
various ambiguities such as ASR ambiguity, intent ambiguity, and hypothesis ambiguity.
When ambiguities are detected, the agent should engage in a clarifying dialog to resolve the
ambiguities before committing to actions. However, asking clarifying questions for all the
ambiguity occurrences could lead to asking too many questions, essentially hampering the
user experience. To trigger clarifying questions only when necessary for the user …
A large-scale conversational agent can suffer from understanding user utterances with various ambiguities such as ASR ambiguity, intent ambiguity, and hypothesis ambiguity. When ambiguities are detected, the agent should engage in a clarifying dialog to resolve the ambiguities before committing to actions. However, asking clarifying questions for all the ambiguity occurrences could lead to asking too many questions, essentially hampering the user experience. To trigger clarifying questions only when necessary for the user satisfaction, we propose a neural self-attentive model that leverages the hypotheses with ambiguities and contextual signals. We conduct extensive experiments on five common ambiguity types using real data from a large-scale commercial conversational agent and demonstrate significant improvement over a set of baseline approaches.
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