1,645 research outputs found
MojiTalk: Generating Emotional Responses at Scale
Generating emotional language is a key step towards building empathetic
natural language processing agents. However, a major challenge for this line of
research is the lack of large-scale labeled training data, and previous studies
are limited to only small sets of human annotated sentiment labels.
Additionally, explicitly controlling the emotion and sentiment of generated
text is also difficult. In this paper, we take a more radical approach: we
exploit the idea of leveraging Twitter data that are naturally labeled with
emojis. More specifically, we collect a large corpus of Twitter conversations
that include emojis in the response, and assume the emojis convey the
underlying emotions of the sentence. We then introduce a reinforced conditional
variational encoder approach to train a deep generative model on these
conversations, which allows us to use emojis to control the emotion of the
generated text. Experimentally, we show in our quantitative and qualitative
analyses that the proposed models can successfully generate high-quality
abstractive conversation responses in accordance with designated emotions
Learning a Policy for Opportunistic Active Learning
Active learning identifies data points to label that are expected to be the
most useful in improving a supervised model. Opportunistic active learning
incorporates active learning into interactive tasks that constrain possible
queries during interactions. Prior work has shown that opportunistic active
learning can be used to improve grounding of natural language descriptions in
an interactive object retrieval task. In this work, we use reinforcement
learning for such an object retrieval task, to learn a policy that effectively
trades off task completion with model improvement that would benefit future
tasks.Comment: EMNLP 2018 Camera Read
Seamlessly Unifying Attributes and Items: Conversational Recommendation for Cold-Start Users
Static recommendation methods like collaborative filtering suffer from the
inherent limitation of performing real-time personalization for cold-start
users. Online recommendation, e.g., multi-armed bandit approach, addresses this
limitation by interactively exploring user preference online and pursuing the
exploration-exploitation (EE) trade-off. However, existing bandit-based methods
model recommendation actions homogeneously. Specifically, they only consider
the items as the arms, being incapable of handling the item attributes, which
naturally provide interpretable information of user's current demands and can
effectively filter out undesired items. In this work, we consider the
conversational recommendation for cold-start users, where a system can both ask
the attributes from and recommend items to a user interactively. This important
scenario was studied in a recent work. However, it employs a hand-crafted
function to decide when to ask attributes or make recommendations. Such
separate modeling of attributes and items makes the effectiveness of the system
highly rely on the choice of the hand-crafted function, thus introducing
fragility to the system. To address this limitation, we seamlessly unify
attributes and items in the same arm space and achieve their EE trade-offs
automatically using the framework of Thompson Sampling. Our Conversational
Thompson Sampling (ConTS) model holistically solves all questions in
conversational recommendation by choosing the arm with the maximal reward to
play. Extensive experiments on three benchmark datasets show that ConTS
outperforms the state-of-the-art methods Conversational UCB (ConUCB) and
Estimation-Action-Reflection model in both metrics of success rate and average
number of conversation turns.Comment: TOIS 202
Towards Improving Proactive Dialog Agents Using Socially-Aware Reinforcement Learning
The next step for intelligent dialog agents is to escape their role as silent
bystanders and become proactive. Well-defined proactive behavior may improve
human-machine cooperation, as the agent takes a more active role during
interaction and takes off responsibility from the user. However, proactivity is
a double-edged sword because poorly executed pre-emptive actions may have a
devastating effect not only on the task outcome but also on the relationship
with the user. For designing adequate proactive dialog strategies, we propose a
novel approach including both social as well as task-relevant features in the
dialog. Here, the primary goal is to optimize proactive behavior so that it is
task-oriented - this implies high task success and efficiency - while also
being socially effective by fostering user trust. Including both aspects in the
reward function for training a proactive dialog agent using reinforcement
learning showed the benefit of our approach for more successful human-machine
cooperation
Survey on Evaluation Methods for Dialogue Systems
In this paper we survey the methods and concepts developed for the evaluation
of dialogue systems. Evaluation is a crucial part during the development
process. Often, dialogue systems are evaluated by means of human evaluations
and questionnaires. However, this tends to be very cost and time intensive.
Thus, much work has been put into finding methods, which allow to reduce the
involvement of human labour. In this survey, we present the main concepts and
methods. For this, we differentiate between the various classes of dialogue
systems (task-oriented dialogue systems, conversational dialogue systems, and
question-answering dialogue systems). We cover each class by introducing the
main technologies developed for the dialogue systems and then by presenting the
evaluation methods regarding this class
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