17,288 research outputs found
An Affect-Rich Neural Conversational Model with Biased Attention and Weighted Cross-Entropy Loss
Affect conveys important implicit information in human communication. Having
the capability to correctly express affect during human-machine conversations
is one of the major milestones in artificial intelligence. In recent years,
extensive research on open-domain neural conversational models has been
conducted. However, embedding affect into such models is still under explored.
In this paper, we propose an end-to-end affect-rich open-domain neural
conversational model that produces responses not only appropriate in syntax and
semantics, but also with rich affect. Our model extends the Seq2Seq model and
adopts VAD (Valence, Arousal and Dominance) affective notations to embed each
word with affects. In addition, our model considers the effect of negators and
intensifiers via a novel affective attention mechanism, which biases attention
towards affect-rich words in input sentences. Lastly, we train our model with
an affect-incorporated objective function to encourage the generation of
affect-rich words in the output responses. Evaluations based on both perplexity
and human evaluations show that our model outperforms the state-of-the-art
baseline model of comparable size in producing natural and affect-rich
responses.Comment: AAAI-1
I Probe, Therefore I Am: Designing a Virtual Journalist with Human Emotions
By utilizing different communication channels, such as verbal language,
gestures or facial expressions, virtually embodied interactive humans hold a
unique potential to bridge the gap between human-computer interaction and
actual interhuman communication. The use of virtual humans is consequently
becoming increasingly popular in a wide range of areas where such a natural
communication might be beneficial, including entertainment, education, mental
health research and beyond. Behind this development lies a series of
technological advances in a multitude of disciplines, most notably natural
language processing, computer vision, and speech synthesis. In this paper we
discuss a Virtual Human Journalist, a project employing a number of novel
solutions from these disciplines with the goal to demonstrate their viability
by producing a humanoid conversational agent capable of naturally eliciting and
reacting to information from a human user. A set of qualitative and
quantitative evaluation sessions demonstrated the technical feasibility of the
system whilst uncovering a number of deficits in its capacity to engage users
in a way that would be perceived as natural and emotionally engaging. We argue
that naturalness should not always be seen as a desirable goal and suggest that
deliberately suppressing the naturalness of virtual human interactions, such as
by altering its personality cues, might in some cases yield more desirable
results.Comment: eNTERFACE16 proceeding
A Personalized System for Conversational Recommendations
Searching for and making decisions about information is becoming increasingly
difficult as the amount of information and number of choices increases.
Recommendation systems help users find items of interest of a particular type,
such as movies or restaurants, but are still somewhat awkward to use. Our
solution is to take advantage of the complementary strengths of personalized
recommendation systems and dialogue systems, creating personalized aides. We
present a system -- the Adaptive Place Advisor -- that treats item selection as
an interactive, conversational process, with the program inquiring about item
attributes and the user responding. Individual, long-term user preferences are
unobtrusively obtained in the course of normal recommendation dialogues and
used to direct future conversations with the same user. We present a novel user
model that influences both item search and the questions asked during a
conversation. We demonstrate the effectiveness of our system in significantly
reducing the time and number of interactions required to find a satisfactory
item, as compared to a control group of users interacting with a non-adaptive
version of the system
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