49 research outputs found
Spoken dialog systems based on online generated stochastic finite-state transducers
This is the author’s version of a work that was accepted for publication in Speech Communication. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Speech Communication 83 (2016) 81–93. DOI 10.1016/j.specom.2016.07.011.In this paper, we present an approach for the development of spoken dialog systems based on the statistical
modelization of the dialog manager. This work focuses on three points: the modelization of the
dialog manager using Stochastic Finite-State Transducers, an unsupervised way to generate training corpora,
and a mechanism to address the problem of coverage that is based on the online generation of
synthetic dialogs. Our proposal has been developed and applied to a sport facilities booking task at the
university. We present experimentation evaluating the system behavior on a set of dialogs that was acquired
using the Wizard of Oz technique as well as experimentation with real users. The experimentation
shows that the method proposed to increase the coverage of the Dialog System was useful to find new
valid paths in the model to achieve the user goals, providing good results with real users.
© 2016 Elsevier B.V. All rights reserved.This work is partially supported by the project ASLP-MULAN: Audio, Speech and Language Processing for Multimedia Analytics (MINECO TIN2014-54288-C4-3-R).Hurtado Oliver, LF.; Planells Lerma, J.; Segarra Soriano, E.; SanchÃs Arnal, E. (2016). Spoken dialog systems based on online generated stochastic finite-state transducers. Speech Communication. 83:81-93. https://doi.org/10.1016/j.specom.2016.07.011S81938
InstructTODS: Large Language Models for End-to-End Task-Oriented Dialogue Systems
Large language models (LLMs) have been used for diverse tasks in natural
language processing (NLP), yet remain under-explored for task-oriented dialogue
systems (TODS), especially for end-to-end TODS. We present InstructTODS, a
novel off-the-shelf framework for zero-shot end-to-end task-oriented dialogue
systems that can adapt to diverse domains without fine-tuning. By leveraging
LLMs, InstructTODS generates a proxy belief state that seamlessly translates
user intentions into dynamic queries for efficient interaction with any KB. Our
extensive experiments demonstrate that InstructTODS achieves comparable
performance to fully fine-tuned TODS in guiding dialogues to successful
completion without prior knowledge or task-specific data. Furthermore, a
rigorous human evaluation of end-to-end TODS shows that InstructTODS produces
dialogue responses that notably outperform both the gold responses and the
state-of-the-art TODS in terms of helpfulness, informativeness, and humanness.
Moreover, the effectiveness of LLMs in TODS is further supported by our
comprehensive evaluations on TODS subtasks: dialogue state tracking, intent
classification, and response generation. Code and implementations could be
found here https://github.com/WillyHC22/InstructTODS