2,246 research outputs found
Conversational Word Embedding for Retrieval-Based Dialog System
Human conversations contain many types of information, e.g., knowledge,
common sense, and language habits. In this paper, we propose a conversational
word embedding method named PR-Embedding, which utilizes the conversation pairs
to learn word embedding. Different
from previous works, PR-Embedding uses the vectors from two different semantic
spaces to represent the words in post and reply. To catch the information among
the pair, we first introduce the word alignment model from statistical machine
translation to generate the cross-sentence window, then train the embedding on
word-level and sentence-level. We evaluate the method on single-turn and
multi-turn response selection tasks for retrieval-based dialog systems. The
experiment results show that PR-Embedding can improve the quality of the
selected response. PR-Embedding source code is available at
https://github.com/wtma/PR-EmbeddingComment: To appear at ACL 202
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
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