671 research outputs found
Individual and Domain Adaptation in Sentence Planning for Dialogue
One of the biggest challenges in the development and deployment of spoken
dialogue systems is the design of the spoken language generation module. This
challenge arises from the need for the generator to adapt to many features of
the dialogue domain, user population, and dialogue context. A promising
approach is trainable generation, which uses general-purpose linguistic
knowledge that is automatically adapted to the features of interest, such as
the application domain, individual user, or user group. In this paper we
present and evaluate a trainable sentence planner for providing restaurant
information in the MATCH dialogue system. We show that trainable sentence
planning can produce complex information presentations whose quality is
comparable to the output of a template-based generator tuned to this domain. We
also show that our method easily supports adapting the sentence planner to
individuals, and that the individualized sentence planners generally perform
better than models trained and tested on a population of individuals. Previous
work has documented and utilized individual preferences for content selection,
but to our knowledge, these results provide the first demonstration of
individual preferences for sentence planning operations, affecting the content
order, discourse structure and sentence structure of system responses. Finally,
we evaluate the contribution of different feature sets, and show that, in our
application, n-gram features often do as well as features based on higher-level
linguistic representations
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
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