68 research outputs found
"How May I Help You?": Modeling Twitter Customer Service Conversations Using Fine-Grained Dialogue Acts
Given the increasing popularity of customer service dialogue on Twitter,
analysis of conversation data is essential to understand trends in customer and
agent behavior for the purpose of automating customer service interactions. In
this work, we develop a novel taxonomy of fine-grained "dialogue acts"
frequently observed in customer service, showcasing acts that are more suited
to the domain than the more generic existing taxonomies. Using a sequential
SVM-HMM model, we model conversation flow, predicting the dialogue act of a
given turn in real-time. We characterize differences between customer and agent
behavior in Twitter customer service conversations, and investigate the effect
of testing our system on different customer service industries. Finally, we use
a data-driven approach to predict important conversation outcomes: customer
satisfaction, customer frustration, and overall problem resolution. We show
that the type and location of certain dialogue acts in a conversation have a
significant effect on the probability of desirable and undesirable outcomes,
and present actionable rules based on our findings. The patterns and rules we
derive can be used as guidelines for outcome-driven automated customer service
platforms.Comment: 13 pages, 6 figures, IUI 201
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
Spatio-temporal variation of conversational utterances on Twitter
Conversations reflect the existing norms of a language. Previously, we found
that utterance lengths in English fictional conversations in books and movies
have shortened over a period of 200 years. In this work, we show that this
shortening occurs even for a brief period of 3 years (September 2009-December
2012) using 229 million utterances from Twitter. Furthermore, the subset of
geographically-tagged tweets from the United States show an inverse proportion
between utterance lengths and the state-level percentage of the Black
population. We argue that shortening of utterances can be explained by the
increasing usage of jargon including coined words.Comment: 13 pages, 7 figures, published in PLoS On
Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models
We investigate the task of building open domain, conversational dialogue
systems based on large dialogue corpora using generative models. Generative
models produce system responses that are autonomously generated word-by-word,
opening up the possibility for realistic, flexible interactions. In support of
this goal, we extend the recently proposed hierarchical recurrent
encoder-decoder neural network to the dialogue domain, and demonstrate that
this model is competitive with state-of-the-art neural language models and
back-off n-gram models. We investigate the limitations of this and similar
approaches, and show how its performance can be improved by bootstrapping the
learning from a larger question-answer pair corpus and from pretrained word
embeddings.Comment: 8 pages with references; Published in AAAI 2016 (Special Track on
Cognitive Systems
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