943 research outputs found
Conversation as Action Under Uncertainty
Conversations abound with uncetainties of various kinds. Treating
conversation as inference and decision making under uncertainty, we propose a
task independent, multimodal architecture for supporting robust continuous
spoken dialog called Quartet. We introduce four interdependent levels of
analysis, and describe representations, inference procedures, and decision
strategies for managing uncertainties within and between the levels. We
highlight the approach by reviewing interactions between a user and two spoken
dialog systems developed using the Quartet architecture: Prsenter, a prototype
system for navigating Microsoft PowerPoint presentations, and the Bayesian
Receptionist, a prototype system for dealing with tasks typically handled by
front desk receptionists at the Microsoft corporate campus.Comment: Appears in Proceedings of the Sixteenth Conference on Uncertainty in
Artificial Intelligence (UAI2000
An End-to-End Trainable Neural Network Model with Belief Tracking for Task-Oriented Dialog
We present a novel end-to-end trainable neural network model for
task-oriented dialog systems. The model is able to track dialog state, issue
API calls to knowledge base (KB), and incorporate structured KB query results
into system responses to successfully complete task-oriented dialogs. The
proposed model produces well-structured system responses by jointly learning
belief tracking and KB result processing conditioning on the dialog history. We
evaluate the model in a restaurant search domain using a dataset that is
converted from the second Dialog State Tracking Challenge (DSTC2) corpus.
Experiment results show that the proposed model can robustly track dialog state
given the dialog history. Moreover, our model demonstrates promising results in
producing appropriate system responses, outperforming prior end-to-end
trainable neural network models using per-response accuracy evaluation metrics.Comment: Published at Interspeech 201
Memory-augmented Dialogue Management for Task-oriented Dialogue Systems
Dialogue management (DM) decides the next action of a dialogue system
according to the current dialogue state, and thus plays a central role in
task-oriented dialogue systems. Since dialogue management requires to have
access to not only local utterances, but also the global semantics of the
entire dialogue session, modeling the long-range history information is a
critical issue. To this end, we propose a novel Memory-Augmented Dialogue
management model (MAD) which employs a memory controller and two additional
memory structures, i.e., a slot-value memory and an external memory. The
slot-value memory tracks the dialogue state by memorizing and updating the
values of semantic slots (for instance, cuisine, price, and location), and the
external memory augments the representation of hidden states of traditional
recurrent neural networks through storing more context information. To update
the dialogue state efficiently, we also propose slot-level attention on user
utterances to extract specific semantic information for each slot. Experiments
show that our model can obtain state-of-the-art performance and outperforms
existing baselines.Comment: 25 pages, 9 figures, Under review of ACM Transactions on Information
Systems (TOIS
A Survey on Dialog Management: Recent Advances and Challenges
Dialog management (DM) is a crucial component in a task-oriented dialog
system. Given the dialog history, DM predicts the dialog state and decides the
next action that the dialog agent should take. Recently, dialog policy learning
has been widely formulated as a Reinforcement Learning (RL) problem, and more
works focus on the applicability of DM. In this paper, we survey recent
advances and challenges within three critical topics for DM: (1) improving
model scalability to facilitate dialog system modeling in new scenarios, (2)
dealing with the data scarcity problem for dialog policy learning, and (3)
enhancing the training efficiency to achieve better task-completion performance
. We believe that this survey can shed a light on future research in dialog
management
Clustering of syntactic and discursive information for the dynamic adaptation of Language Models
Presentamos una estrategia de agrupamiento de elementos de diálogo, de tipo semántico y discursivo. Empleando Latent Semantic Analysis (LSA) agru- pamos los diferentes elementos de acuerdo a un criterio de distancia basado en correlación. Tras seleccionar un conjunto de grupos que forman una partición del espacio semántico o discursivo considerado, entrenamos unos modelos de lenguaje estocásticos (LM) asociados a cada modelo. Dichos modelos se emplearán en la adaptación dinámica del modelo de lenguaje empleado por el reconocedor de habla incluido en un sistema de diálogo. Mediante el empleo de información de diálogo (las probabilidades a posteriori que el gestor de diálogo asigna a cada elemento de diálogo en cada turno), estimamos los pesos de interpolación correspondientes a cada LM. Los experimentos iniciales muestran una reducción de la tasa de error de palabra al emplear la información obtenida a partir de una frase para reestimar la misma frase
Dialogue Act Recognition Approaches
This paper deals with automatic dialogue act (DA) recognition. Dialogue acts are sentence-level units that represent states of a dialogue, such as questions, statements, hesitations, etc. The knowledge of dialogue act realizations in a discourse or dialogue is part of the speech understanding and dialogue analysis process. It is of great importance for many applications: dialogue systems, speech recognition, automatic machine translation, etc. The main goal of this paper is to study the existing works about DA recognition and to discuss their respective advantages and drawbacks. A major concern in the DA recognition domain is that, although a few DA annotation schemes seem now to emerge as standards, most of the time, these DA tag-sets have to be adapted to the specificities of a given application, which prevents the deployment of standardized DA databases and evaluation procedures. The focus of this review is put on the various kinds of information that can be used to recognize DAs, such as prosody, lexical, etc., and on the types of models proposed so far to capture this information. Combining these information sources tends to appear nowadays as a prerequisite to recognize DAs
The Bottleneck Simulator: A Model-based Deep Reinforcement Learning Approach
Deep reinforcement learning has recently shown many impressive successes.
However, one major obstacle towards applying such methods to real-world
problems is their lack of data-efficiency. To this end, we propose the
Bottleneck Simulator: a model-based reinforcement learning method which
combines a learned, factorized transition model of the environment with rollout
simulations to learn an effective policy from few examples. The learned
transition model employs an abstract, discrete (bottleneck) state, which
increases sample efficiency by reducing the number of model parameters and by
exploiting structural properties of the environment. We provide a mathematical
analysis of the Bottleneck Simulator in terms of fixed points of the learned
policy, which reveals how performance is affected by four distinct sources of
error: an error related to the abstract space structure, an error related to
the transition model estimation variance, an error related to the transition
model estimation bias, and an error related to the transition model class bias.
Finally, we evaluate the Bottleneck Simulator on two natural language
processing tasks: a text adventure game and a real-world, complex dialogue
response selection task. On both tasks, the Bottleneck Simulator yields
excellent performance beating competing approaches.Comment: 26 pages, 2 figures, 4 table
A Latent Variable Recurrent Neural Network for Discourse Relation Language Models
This paper presents a novel latent variable recurrent neural network
architecture for jointly modeling sequences of words and (possibly latent)
discourse relations between adjacent sentences. A recurrent neural network
generates individual words, thus reaping the benefits of
discriminatively-trained vector representations. The discourse relations are
represented with a latent variable, which can be predicted or marginalized,
depending on the task. The resulting model can therefore employ a training
objective that includes not only discourse relation classification, but also
word prediction. As a result, it outperforms state-of-the-art alternatives for
two tasks: implicit discourse relation classification in the Penn Discourse
Treebank, and dialog act classification in the Switchboard corpus. Furthermore,
by marginalizing over latent discourse relations at test time, we obtain a
discourse informed language model, which improves over a strong LSTM baseline.Comment: NAACL 2016 camera ready, 11 page
Intelligent Tutoring Systems: A Comprehensive Historical Survey with Recent Developments
This paper provides interested beginners with an updated and detailed
introduction to the field of Intelligent Tutoring Systems (ITS). ITSs are
computer programs that use artificial intelligence techniques to enhance and
personalize automation in teaching. This paper is a literature review that
provides the following: First, a review of the history of ITS along with a
discussion on the interface between human learning and computer tutors and how
effective ITSs are in contemporary education. Second, the traditional
architectural components of an ITS and their functions are discussed along with
approaches taken by various ITSs. Finally, recent innovative ideas in ITS
systems are presented. This paper concludes with some of the author's views
regarding future work in the field of intelligent tutoring systems
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