13,630 research outputs found
Out-of-domain Detection for Natural Language Understanding in Dialog Systems
Natural Language Understanding (NLU) is a vital component of dialogue
systems, and its ability to detect Out-of-Domain (OOD) inputs is critical in
practical applications, since the acceptance of the OOD input that is
unsupported by the current system may lead to catastrophic failure. However,
most existing OOD detection methods rely heavily on manually labeled OOD
samples and cannot take full advantage of unlabeled data. This limits the
feasibility of these models in practical applications.
In this paper, we propose a novel model to generate high-quality pseudo OOD
samples that are akin to IN-Domain (IND) input utterances, and thereby improves
the performance of OOD detection. To this end, an autoencoder is trained to map
an input utterance into a latent code. and the codes of IND and OOD samples are
trained to be indistinguishable by utilizing a generative adversarial network.
To provide more supervision signals, an auxiliary classifier is introduced to
regularize the generated OOD samples to have indistinguishable intent labels.
Experiments show that these pseudo OOD samples generated by our model can be
used to effectively improve OOD detection in NLU. Besides, we also demonstrate
that the effectiveness of these pseudo OOD data can be further improved by
efficiently utilizing unlabeled data.Comment: Accepted by TALS
The use of belief networks in natural language understanding and dialog modeling.
Wai, Chi Man Carmen.Thesis (M.Phil.)--Chinese University of Hong Kong, 2001.Includes bibliographical references (leaves 129-136).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Overview --- p.1Chapter 1.2 --- Natural Language Understanding --- p.3Chapter 1.3 --- BNs for Handling Speech Recognition Errors --- p.4Chapter 1.4 --- BNs for Dialog Modeling --- p.5Chapter 1.5 --- Thesis Goals --- p.8Chapter 1.6 --- Thesis Outline --- p.8Chapter 2 --- Background --- p.10Chapter 2.1 --- Natural Language Understanding --- p.11Chapter 2.1.1 --- Rule-based Approaches --- p.12Chapter 2.1.2 --- Stochastic Approaches --- p.13Chapter 2.1.3 --- Phrase-Spotting Approaches --- p.16Chapter 2.2 --- Handling Recognition Errors in Spoken Queries --- p.17Chapter 2.3 --- Spoken Dialog Systems --- p.19Chapter 2.3.1 --- Finite-State Networks --- p.21Chapter 2.3.2 --- The Form-based Approaches --- p.21Chapter 2.3.3 --- Sequential Decision Approaches --- p.22Chapter 2.3.4 --- Machine Learning Approaches --- p.24Chapter 2.4 --- Belief Networks --- p.27Chapter 2.4.1 --- Introduction --- p.27Chapter 2.4.2 --- Bayesian Inference --- p.29Chapter 2.4.3 --- Applications of the Belief Networks --- p.32Chapter 2.5 --- Chapter Summary --- p.33Chapter 3 --- Belief Networks for Natural Language Understanding --- p.34Chapter 3.1 --- The ATIS Domain --- p.35Chapter 3.2 --- Problem Formulation --- p.36Chapter 3.3 --- Semantic Tagging --- p.37Chapter 3.4 --- Belief Networks Development --- p.38Chapter 3.4.1 --- Concept Selection --- p.39Chapter 3.4.2 --- Bayesian Inferencing --- p.40Chapter 3.4.3 --- Thresholding --- p.40Chapter 3.4.4 --- Goal Identification --- p.41Chapter 3.5 --- Experiments on Natural Language Understanding --- p.42Chapter 3.5.1 --- Comparison between Mutual Information and Informa- tion Gain --- p.42Chapter 3.5.2 --- Varying the Input Dimensionality --- p.44Chapter 3.5.3 --- Multiple Goals and Rejection --- p.46Chapter 3.5.4 --- Comparing Grammars --- p.47Chapter 3.6 --- Benchmark with Decision Trees --- p.48Chapter 3.7 --- Performance on Natural Language Understanding --- p.51Chapter 3.8 --- Handling Speech Recognition Errors in Spoken Queries --- p.52Chapter 3.8.1 --- Corpus Preparation --- p.53Chapter 3.8.2 --- Enhanced Belief Network Topology --- p.54Chapter 3.8.3 --- BNs for Handling Speech Recognition Errors --- p.55Chapter 3.8.4 --- Experiments on Handling Speech Recognition Errors --- p.60Chapter 3.8.5 --- Significance Testing --- p.64Chapter 3.8.6 --- Error Analysis --- p.65Chapter 3.9 --- Chapter Summary --- p.67Chapter 4 --- Belief Networks for Mixed-Initiative Dialog Modeling --- p.68Chapter 4.1 --- The CU FOREX Domain --- p.69Chapter 4.1.1 --- Domain-Specific Constraints --- p.69Chapter 4.1.2 --- Two Interaction Modalities --- p.70Chapter 4.2 --- The Belief Networks --- p.70Chapter 4.2.1 --- Informational Goal Inference --- p.72Chapter 4.2.2 --- Detection of Missing / Spurious Concepts --- p.74Chapter 4.3 --- Integrating Two Interaction Modalities --- p.78Chapter 4.4 --- Incorporating Out-of-Vocabulary Words --- p.80Chapter 4.4.1 --- Natural Language Queries --- p.80Chapter 4.4.2 --- Directed Queries --- p.82Chapter 4.5 --- Evaluation of the BN-based Dialog Model --- p.84Chapter 4.6 --- Chapter Summary --- p.87Chapter 5 --- Scalability and Portability of Belief Network-based Dialog Model --- p.88Chapter 5.1 --- Migration to the ATIS Domain --- p.89Chapter 5.2 --- Scalability of the BN-based Dialog Model --- p.90Chapter 5.2.1 --- Informational Goal Inference --- p.90Chapter 5.2.2 --- Detection of Missing / Spurious Concepts --- p.92Chapter 5.2.3 --- Context Inheritance --- p.94Chapter 5.3 --- Portability of the BN-based Dialog Model --- p.101Chapter 5.3.1 --- General Principles for Probability Assignment --- p.101Chapter 5.3.2 --- Performance of the BN-based Dialog Model with Hand- Assigned Probabilities --- p.105Chapter 5.3.3 --- Error Analysis --- p.108Chapter 5.4 --- Enhancements for Discourse Query Understanding --- p.110Chapter 5.4.1 --- Combining Trained and Handcrafted Probabilities --- p.110Chapter 5.4.2 --- Handcrafted Topology for BNs --- p.111Chapter 5.4.3 --- Performance of the Enhanced BN-based Dialog Model --- p.117Chapter 5.5 --- Chapter Summary --- p.120Chapter 6 --- Conclusions --- p.122Chapter 6.1 --- Summary --- p.122Chapter 6.2 --- Contributions --- p.126Chapter 6.3 --- Future Work --- p.127Bibliography --- p.129Chapter A --- The Two Original SQL Query --- p.137Chapter B --- "The Two Grammars, GH and GsA" --- p.139Chapter C --- Probability Propagation in Belief Networks --- p.149Chapter C.1 --- Computing the aposteriori probability of P*(G) based on in- put concepts --- p.151Chapter C.2 --- Computing the aposteriori probability of P*(Cj) by backward inference --- p.154Chapter D --- Total 23 Concepts for the Handcrafted BN --- p.15
Combining Expression and Content in Domains for Dialog Managers
We present work in progress on abstracting dialog managers from their domain
in order to implement a dialog manager development tool which takes (among
other data) a domain description as input and delivers a new dialog manager for
the described domain as output. Thereby we will focus on two topics; firstly,
the construction of domain descriptions with description logics and secondly,
the interpretation of utterances in a given domain.Comment: 5 pages, uses conference.st
SCREEN: Learning a Flat Syntactic and Semantic Spoken Language Analysis Using Artificial Neural Networks
In this paper, we describe a so-called screening approach for learning robust
processing of spontaneously spoken language. A screening approach is a flat
analysis which uses shallow sequences of category representations for analyzing
an utterance at various syntactic, semantic and dialog levels. Rather than
using a deeply structured symbolic analysis, we use a flat connectionist
analysis. This screening approach aims at supporting speech and language
processing by using (1) data-driven learning and (2) robustness of
connectionist networks. In order to test this approach, we have developed the
SCREEN system which is based on this new robust, learned and flat analysis.
In this paper, we focus on a detailed description of SCREEN's architecture,
the flat syntactic and semantic analysis, the interaction with a speech
recognizer, and a detailed evaluation analysis of the robustness under the
influence of noisy or incomplete input. The main result of this paper is that
flat representations allow more robust processing of spontaneous spoken
language than deeply structured representations. In particular, we show how the
fault-tolerance and learning capability of connectionist networks can support a
flat analysis for providing more robust spoken-language processing within an
overall hybrid symbolic/connectionist framework.Comment: 51 pages, Postscript. To be published in Journal of Artificial
Intelligence Research 6(1), 199
- …