2 research outputs found
Zero-shot Multi-Domain Dialog State Tracking Using Descriptive Rules
In this work, we present a framework for incorporating descriptive logical rules in state-of-the-art neural networks, enabling them to learn how to handle unseen labels without the introduction of any new training data. The rules are integrated into existing networks without modifying their architecture, through an additional term in the network鈥檚 loss function that penalizes states of the network that do not obey the designed rules.As a case of study, the framework is applied to an existing neuralbased Dialog State Tracker. Our experiments demonstrate that the inclusion of logical rules allows the prediction of unseen labels, without deteriorating the predictive capacity of the original system.Fil: Altszyler Lemcovich, Edgar Jaim. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computaci贸n; Argentina. Consejo Nacional de Investigaciones Cient铆ficas y T茅cnicas. Oficina de Coordinaci贸n Administrativa Ciudad Universitaria. Instituto de Investigaci贸n en Ciencias de la Computaci贸n. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaci贸n en Ciencias de la Computaci贸n; ArgentinaFil: Brusco, Pablo. Consejo Nacional de Investigaciones Cient铆ficas y T茅cnicas. Oficina de Coordinaci贸n Administrativa Ciudad Universitaria. Instituto de Investigaci贸n en Ciencias de la Computaci贸n. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaci贸n en Ciencias de la Computaci贸n; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computaci贸n; ArgentinaFil: Basiou, Nikoletta. Sri International; Estados UnidosFil: Byrnes, John. Sri International; Estados UnidosFil: Vergyri, Dimitra. Sri International; Estados Unido
Multi-User MultiWOZ: Task-Oriented Dialogues among Multiple Users
While most task-oriented dialogues assume conversations between the agent and
one user at a time, dialogue systems are increasingly expected to communicate
with multiple users simultaneously who make decisions collaboratively. To
facilitate development of such systems, we release the Multi-User MultiWOZ
dataset: task-oriented dialogues among two users and one agent. To collect this
dataset, each user utterance from MultiWOZ 2.2 was replaced with a small chat
between two users that is semantically and pragmatically consistent with the
original user utterance, thus resulting in the same dialogue state and system
response. These dialogues reflect interesting dynamics of collaborative
decision-making in task-oriented scenarios, e.g., social chatter and
deliberation. Supported by this data, we propose the novel task of multi-user
contextual query rewriting: to rewrite a task-oriented chat between two users
as a concise task-oriented query that retains only task-relevant information
and that is directly consumable by the dialogue system. We demonstrate that in
multi-user dialogues, using predicted rewrites substantially improves dialogue
state tracking without modifying existing dialogue systems that are trained for
single-user dialogues. Further, this method surpasses training a medium-sized
model directly on multi-user dialogues and generalizes to unseen domains.Comment: To Appear in EMNLP-Findings 202