A speech understanding system based on statistical representation of semantics

Pieraccini, Roberto; Tzoukermann, Evelyne; Gorelov, Zakhar; Gauvain, Jean‐Luc; Levin, Esther; Lee, C.-H.; Wilpon, J. G.

doi:10.1109/icassp.1992.225939

Cited by 67 publications

(34 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Then slot filling is framed as a word sequence tagging task, where the IOB (in-out-begin) format is applied for representing slot tags, and hidden Markov models (HMM) or conditional random fields (CRF) have been employed for slot tagging (Pieraccini et al, 1992;Wang et al, 2005;Raymond and Riccardi, 2007).…”

Section: Dialogue System Basicsmentioning

confidence: 99%

Deep Learning for Dialogue Systems

Chen¹,

Çelikyılmaz²,

Hakkani‐Tür³

2017

Proceedings of ACL 2017, Tutorial Abstracts

View full text Add to dashboard Cite

In the past decade, goal-oriented spoken dialogue systems have been the most prominent component in today's virtual personal assistants. The classic dialogue systems have rather complex and/or modular pipelines. The advance of deep learning technologies has recently risen the applications of neural models to dialogue modeling. However, how to successfully apply deep learning based approaches to a dialogue system is still challenging. Hence, this tutorial is designed to focus on an overview of the dialogue system development while describing most recent research for building dialogue systems and summarizing the challenges, in order to allow researchers to study the potential improvements of the state-of-the-art dialogue systems. The tutorial material is available at http://deepdialogue. miulab.tw. Tutorial OverviewWith the rising trend of artificial intelligence, more and more devices have incorporated goal-oriented spoken dialogue systems. Among popular virtual personal assistants, Microsoft's Cortana, Apple's Siri, Amazon Alexa, Google Assistant, and Facebook's M, have incorporated dialogue system modules in various devices, which allow users to speak naturally in order to finish tasks more efficiently.The traditional conversational systems have rather complex and/or modular pipelines. The advance of deep learning technologies has recently risen the applications of neural models to dialogue modeling. Nevertheless, applying deep learning technologies for building robust and scalable dialogue systems is still a challenging task and an open research area as it requires deeper understanding of the classic pipelines as well as detailed knowledge on the benchmark of the models of the prior work and the recent state-of-the-art work.The goal of this tutorial is to provide the audience with developing trend of the dialogue systems, and a roadmap to get them started with the related work. In the first section of the tutorial, we motivate the work on conversation-based intelligent agents, in which the core underlying system is task-oriented dialogue systems. The second and third sections describe different approaches using deep learning for each component in the dialogue system and how it is evaluated. The last two sections focus on discussing the recent trends and current challenges on dialogue system technology and summarize the challenges and conclusions. Then the detailed content is described as follows.2 Outline

show abstract

Section: Dialogue System Basicsmentioning

confidence: 99%

Deep Learning for Dialogue Systems

Chen¹,

Çelikyılmaz²,

Hakkani‐Tür³

2017

Proceedings of ACL 2017, Tutorial Abstracts

View full text Add to dashboard Cite

show abstract

“…Syntax plays a secondary role-to identify modifying phrases or special constructions such as dates. These systems differ in their approach to building the semantic constructs and include recursive transition networks to model the semantic phrases (12,13), template-matching (14), hidden Markov models (HMMs) to segment the input into semantic "chunks" and neural networks or decision trees (15,16). * A syntax-driven approach first identifies syntactic constituents, and semantic processing provides an interpretation based on the syntactic relations.…”

Section: The Dual Role Of Language Processingmentioning

confidence: 99%

The roles of language processing in a spoken language interface.

Hirschman¹

1995

Proc. Natl. Acad. Sci. U.S.A.

View full text Add to dashboard Cite

This paper provides an overview of the colloquium's discussion session on natural language understanding, which followed presentations by M. Bates [Bates, M. (1995) Proc This paper provides an overview of the natural language understanding session at the Colloquium on HumanMachine Communication by Voice held by the National Academy of Sciences (NAS). The aim of the paper is to review the role that language understanding plays in spoken language systems and to summarize the discussion that followed the two presentations by Bates (1) and Moore (2). A number of questions were raised during the discussion, including whether a single system could provide both understanding and constraint, what the future role of discourse should be, how to evaluate performance on interactive systems, and whether we are moving in the right direction toward realizing the goal of interactive human-machine communication.Background: The ARPA Spoken Language Program

show abstract

“…Statistical methods are attractive because they can be easily adapted to new conditions using only annotated training data. Statistical methods for SLU have been studied in a Hidden Vector State (HVS) Model (He et al, 2005) and a data-driven statistical models (Miller et al 1994;Pieraccini et al 1992;Wang et al 2006). In addition, Jeong and Lee (2008) proposed a unified probabilistic model (triangular-chain CRF) combining the named entity and dialog-act of SLU.…”

Section: Related Workmentioning

confidence: 99%

A Simultaneous Recognition Framework for the Spoken Language Understanding Module of Intelligent Personal Assistant Software on Smart Phones

Lee

Seo

2015

Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Confere

View full text Add to dashboard Cite

The intelligent personal assistant software such as the Apple's Siri and Samsung's S-Voice has been issued these days. This paper introduces a novel Spoken Language Understanding (SLU) module to predict user's intention for determining system actions of the intelligent personal assistant software. The SLU module usually consists of several connected recognition tasks on a pipeline framework, whereas the proposed SLU module simultaneously recognizes four recognition tasks on a recognition framework using Conditional Random Fields (CRF). The four tasks include named entity, speech-act, target and operation recognition. In the experiments, the new simultaneous recognition method achieves the higher performance of 4% and faster speed of about 25% than other method using a pipeline framework. By a significance test, this improvement is considered to be statistically significant as a p-value of smaller than 0.05.

show abstract

A speech understanding system based on statistical representation of semantics

Cited by 67 publications

References 10 publications

Deep Learning for Dialogue Systems

Deep Learning for Dialogue Systems

The roles of language processing in a spoken language interface.

A Simultaneous Recognition Framework for the Spoken Language Understanding Module of Intelligent Personal Assistant Software on Smart Phones

Contact Info

Product

Resources

About