65,224 research outputs found
Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings
We study a symmetric collaborative dialogue setting in which two agents, each
with private knowledge, must strategically communicate to achieve a common
goal. The open-ended dialogue state in this setting poses new challenges for
existing dialogue systems. We collected a dataset of 11K human-human dialogues,
which exhibits interesting lexical, semantic, and strategic elements. To model
both structured knowledge and unstructured language, we propose a neural model
with dynamic knowledge graph embeddings that evolve as the dialogue progresses.
Automatic and human evaluations show that our model is both more effective at
achieving the goal and more human-like than baseline neural and rule-based
models.Comment: ACL 201
Adversarial Learning for Neural Dialogue Generation
In this paper, drawing intuition from the Turing test, we propose using
adversarial training for open-domain dialogue generation: the system is trained
to produce sequences that are indistinguishable from human-generated dialogue
utterances. We cast the task as a reinforcement learning (RL) problem where we
jointly train two systems, a generative model to produce response sequences,
and a discriminator---analagous to the human evaluator in the Turing test--- to
distinguish between the human-generated dialogues and the machine-generated
ones. The outputs from the discriminator are then used as rewards for the
generative model, pushing the system to generate dialogues that mostly resemble
human dialogues.
In addition to adversarial training we describe a model for adversarial {\em
evaluation} that uses success in fooling an adversary as a dialogue evaluation
metric, while avoiding a number of potential pitfalls. Experimental results on
several metrics, including adversarial evaluation, demonstrate that the
adversarially-trained system generates higher-quality responses than previous
baselines
Improving Search through A3C Reinforcement Learning based Conversational Agent
We develop a reinforcement learning based search assistant which can assist
users through a set of actions and sequence of interactions to enable them
realize their intent. Our approach caters to subjective search where the user
is seeking digital assets such as images which is fundamentally different from
the tasks which have objective and limited search modalities. Labeled
conversational data is generally not available in such search tasks and
training the agent through human interactions can be time consuming. We propose
a stochastic virtual user which impersonates a real user and can be used to
sample user behavior efficiently to train the agent which accelerates the
bootstrapping of the agent. We develop A3C algorithm based context preserving
architecture which enables the agent to provide contextual assistance to the
user. We compare the A3C agent with Q-learning and evaluate its performance on
average rewards and state values it obtains with the virtual user in validation
episodes. Our experiments show that the agent learns to achieve higher rewards
and better states.Comment: 17 pages, 7 figure
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