12,349 research outputs found
Unsupervised Dialogue Act Induction using Gaussian Mixtures
This paper introduces a new unsupervised approach for dialogue act induction.
Given the sequence of dialogue utterances, the task is to assign them the
labels representing their function in the dialogue.
Utterances are represented as real-valued vectors encoding their meaning. We
model the dialogue as Hidden Markov model with emission probabilities estimated
by Gaussian mixtures. We use Gibbs sampling for posterior inference.
We present the results on the standard Switchboard-DAMSL corpus. Our
algorithm achieves promising results compared with strong supervised baselines
and outperforms other unsupervised algorithms.Comment: Accepted to EACL 201
A Knowledge-Grounded Multimodal Search-Based Conversational Agent
Multimodal search-based dialogue is a challenging new task: It extends
visually grounded question answering systems into multi-turn conversations with
access to an external database. We address this new challenge by learning a
neural response generation system from the recently released Multimodal
Dialogue (MMD) dataset (Saha et al., 2017). We introduce a knowledge-grounded
multimodal conversational model where an encoded knowledge base (KB)
representation is appended to the decoder input. Our model substantially
outperforms strong baselines in terms of text-based similarity measures (over 9
BLEU points, 3 of which are solely due to the use of additional information
from the KB
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