207,672 research outputs found
Frames: A Corpus for Adding Memory to Goal-Oriented Dialogue Systems
This paper presents the Frames dataset (Frames is available at
http://datasets.maluuba.com/Frames), a corpus of 1369 human-human dialogues
with an average of 15 turns per dialogue. We developed this dataset to study
the role of memory in goal-oriented dialogue systems. Based on Frames, we
introduce a task called frame tracking, which extends state tracking to a
setting where several states are tracked simultaneously. We propose a baseline
model for this task. We show that Frames can also be used to study memory in
dialogue management and information presentation through natural language
generation
End-to-end optimization of goal-driven and visually grounded dialogue systems
End-to-end design of dialogue systems has recently become a popular research
topic thanks to powerful tools such as encoder-decoder architectures for
sequence-to-sequence learning. Yet, most current approaches cast human-machine
dialogue management as a supervised learning problem, aiming at predicting the
next utterance of a participant given the full history of the dialogue. This
vision is too simplistic to render the intrinsic planning problem inherent to
dialogue as well as its grounded nature, making the context of a dialogue
larger than the sole history. This is why only chit-chat and question answering
tasks have been addressed so far using end-to-end architectures. In this paper,
we introduce a Deep Reinforcement Learning method to optimize visually grounded
task-oriented dialogues, based on the policy gradient algorithm. This approach
is tested on a dataset of 120k dialogues collected through Mechanical Turk and
provides encouraging results at solving both the problem of generating natural
dialogues and the task of discovering a specific object in a complex picture
Converse: A Tree-Based Modular Task-Oriented Dialogue System
Creating a system that can have meaningful conversations with humans to help
accomplish tasks is one of the ultimate goals of Artificial Intelligence (AI).
It has defined the meaning of AI since the beginning. A lot has been
accomplished in this area recently, with voice assistant products entering our
daily lives and chat bot systems becoming commonplace in customer service. At
first glance there seems to be no shortage of options for dialogue systems.
However, the frequently deployed dialogue systems today seem to all struggle
with a critical weakness - they are hard to build and harder to maintain. At
the core of the struggle is the need to script every single turn of
interactions between the bot and the human user. This makes the dialogue
systems more difficult to maintain as the tasks become more complex and more
tasks are added to the system. In this paper, we propose Converse, a flexible
tree-based modular task-oriented dialogue system. Converse uses an and-or tree
structure to represent tasks and offers powerful multi-task dialogue
management. Converse supports task dependency and task switching, which are
unique features compared to other open-source dialogue frameworks. At the same
time, Converse aims to make the bot building process easy and simple, for both
professional and non-professional software developers. The code is available at
https://github.com/salesforce/Converse
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