950 research outputs found
User Intent Prediction in Information-seeking Conversations
Conversational assistants are being progressively adopted by the general
population. However, they are not capable of handling complicated
information-seeking tasks that involve multiple turns of information exchange.
Due to the limited communication bandwidth in conversational search, it is
important for conversational assistants to accurately detect and predict user
intent in information-seeking conversations. In this paper, we investigate two
aspects of user intent prediction in an information-seeking setting. First, we
extract features based on the content, structural, and sentiment
characteristics of a given utterance, and use classic machine learning methods
to perform user intent prediction. We then conduct an in-depth feature
importance analysis to identify key features in this prediction task. We find
that structural features contribute most to the prediction performance. Given
this finding, we construct neural classifiers to incorporate context
information and achieve better performance without feature engineering. Our
findings can provide insights into the important factors and effective methods
of user intent prediction in information-seeking conversations.Comment: Accepted to CHIIR 201
Multimodal Recommendation Dialog with Subjective Preference: A New Challenge and Benchmark
Existing multimodal task-oriented dialog data fails to demonstrate the
diverse expressions of user subjective preferences and recommendation acts in
the real-life shopping scenario. This paper introduces a new dataset SURE
(Multimodal Recommendation Dialog with SUbjective PREference), which contains
12K shopping dialogs in complex store scenes. The data is built in two phases
with human annotations to ensure quality and diversity. SURE is well-annotated
with subjective preferences and recommendation acts proposed by sales experts.
A comprehensive analysis is given to reveal the distinguishing features of
SURE. Three benchmark tasks are then proposed on the data to evaluate the
capability of multimodal recommendation agents. Based on the SURE, we propose a
baseline model, powered by a state-of-the-art multimodal model, for these
tasks.Comment: ACL 202
Long Short-Term Planning for Conversational Recommendation Systems
In Conversational Recommendation Systems (CRS), the central question is how
the conversational agent can naturally ask for user preferences and provide
suitable recommendations. Existing works mainly follow the hierarchical
architecture, where a higher policy decides whether to invoke the conversation
module (to ask questions) or the recommendation module (to make
recommendations). This architecture prevents these two components from fully
interacting with each other. In contrast, this paper proposes a novel
architecture, the long short-term feedback architecture, to connect these two
essential components in CRS. Specifically, the recommendation predicts the
long-term recommendation target based on the conversational context and the
user history. Driven by the targeted recommendation, the conversational model
predicts the next topic or attribute to verify if the user preference matches
the target. The balance feedback loop continues until the short-term planner
output matches the long-term planner output, that is when the system should
make the recommendation.Comment: 14 pages, 3 figures. Accepted by ICONIP 202
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