scholarly journals Spectral decomposition method of dialog state tracking via collective matrix factorization

2016 ◽  
Vol 7 (3) ◽  
pp. 34-46
Author(s):  
Julien Perez
Keyword(s):  
Matrix Factorization ◽  
The State ◽  
Computationally Efficient ◽  
Reward Function ◽  
Dialog Management ◽  
Dialog System ◽  
Dependent Variables ◽  
Novel Method ◽  
State Tracking ◽  
Dialog State Tracking

The task of dialog management is commonly decomposed into two sequential subtasks: dialog state tracking and dialog policy learning. In an end-to-end dialog system, the aim of dialog state tracking is to accurately estimate the true dialog state from noisy observations produced by the speech recognition and the natural language understanding modules. The state tracking task is primarily meant to support a dialog policy. From a probabilistic perspective, this is achieved by maintaining a posterior distribution over hidden dialog states composed of a set of context dependent variables. Once a dialog policy is learned, it strives to select an optimal dialog act given the estimated dialog state and a defined reward function. This paper introduces a novel method of dialog state tracking based on a bilinear algebric decomposition model that provides an efficient inference schema through collective matrix factorization. We evaluate the proposed approach on the second Dialog State Tracking Challenge (DSTC-2) dataset and we show that the proposed tracker gives encouraging results compared to the state-of-the-art trackers that participated in this standard benchmark. Finally, we show that the prediction schema is computationally efficient in comparison to the previous approaches.

scholarly journals The Dialog State Tracking Challenge Series: A Review

2016 ◽  
Vol 7 (3) ◽  
pp. 4-33 ◽  
Author(s):  
Jason D. Williams ◽  
Antoine Raux ◽  
Matthew Henderson
Keyword(s):  
Speech Recognition ◽  
Research Area ◽  
The State ◽  
Evaluation Metrics ◽  
Common Resources ◽  
Discriminative Models ◽  
Dialog System ◽  
Spoken Dialog System ◽  
State Tracking ◽  
Dialog State Tracking

In a spoken dialog system, dialog state tracking refers to the task of correctly inferring the state of the conversation -- such as the user's goal -- given all of the dialog history up to that turn.  Dialog state tracking is crucial to the success of a dialog system, yet until recently there were no common resources, hampering progress.  The Dialog State Tracking Challenge series of 3 tasks introduced the first shared testbed and evaluation metrics for dialog state tracking, and has underpinned three key advances in dialog state tracking: the move from generative to discriminative models; the adoption of discriminative sequential techniques; and the incorporation of the speech recognition results directly into the dialog state tracker.  This paper reviews this research area, covering both the challenge tasks themselves and summarizing the work they have enabled.

scholarly journals A Two-Step Neural Dialog State Tracker for Task-Oriented Dialog Processing

2018 ◽  
Vol 2018 ◽  
pp. 1-11
Author(s):  
A-Yeong Kim ◽  
Hyun-Je Song ◽  
Seong-Bae Park
Keyword(s):  
Attention Mechanism ◽  
Data Set ◽  
Dialog Systems ◽  
Dialog System ◽  
Fast Training ◽  
Proposed Model ◽  
Spoken Dialog System ◽  
State Tracking ◽  
Dialog State Tracking ◽  
Task Oriented

Dialog state tracking in a spoken dialog system is the task that tracks the flow of a dialog and identifies accurately what a user wants from the utterance. Since the success of a dialog is influenced by the ability of the system to catch the requirements of the user, accurate state tracking is important for spoken dialog systems. This paper proposes a two-step neural dialog state tracker which is composed of an informativeness classifier and a neural tracker. The informativeness classifier which is implemented by a CNN first filters out noninformative utterances in a dialog. Then, the neural tracker estimates dialog states from the remaining informative utterances. The tracker adopts the attention mechanism and the hierarchical softmax for its performance and fast training. To prove the effectiveness of the proposed model, we do experiments on dialog state tracking in the human-human task-oriented dialogs with the standard DSTC4 data set. Our experimental results prove the effectiveness of the proposed model by showing that the proposed model outperforms the neural trackers without the informativeness classifier, the attention mechanism, or the hierarchical softmax.

scholarly journals Recurrent Polynomial Network for Dialogue State Tracking

2016 ◽  
Vol 7 (3) ◽  
pp. 65-88
Author(s):  
Kai Sun ◽  
Qizhe Xie ◽  
Kai Yu
Keyword(s):  
State Of The Art ◽  
The State ◽  
Rule Based ◽  
The Third ◽  
Unique Structure ◽  
State Tracking ◽  
Dialog State Tracking ◽  
Statistical Approaches

  Dialogue state tracking (DST) is a process to estimate the distribution of the dialogue states as a dialogue progresses. Recent studies on constrained Markov Bayesian polynomial (CMBP) framework take the first step towards bridging the gap between rule-based and statistical approaches for DST. In this paper, the gap is further bridged by a novel framework -- recurrent polynomial network (RPN). RPN's unique structure enables the framework to have all the advantages of CMBP including efficiency, portability and interpretability. Additionally, RPN achieves more properties of statistical approaches than CMBP. RPN was evaluated on the data corpora of the second and the third Dialog State Tracking Challenge (DSTC-2/3). Experiments showed that RPN can significantly outperform both traditional rule-based approaches and statistical approaches with similar feature set. Compared with the state-of-the-art statistical DST approaches with a lot richer features, RPN is also competitive.

scholarly journals Dialog History Construction with Long-Short Term Memory for Robust Generative Dialog State Tracking

2016 ◽  
Vol 7 (3) ◽  
pp. 47-64 ◽  
Author(s):  
Byung-Jun Lee ◽  
Kee-Eung Kim
Keyword(s):  
Speech Processing ◽  
Short Term Memory ◽  
Dialog Systems ◽  
Dialog System ◽  
Gradient Descent Algorithm ◽  
Core Areas ◽  
Overall Performance ◽  
Long Short Term Memory ◽  
State Tracking ◽  
Dialog State Tracking

One of the crucial components of dialog system is the dialog state tracker, which infers user’s intention from preliminary speech processing. Since the overall performance of the dialog system is heavily affected by that of the dialog tracker, it has been one of the core areas of research on dialog systems. In this paper, we present a dialog state tracker that combines a generative probabilistic model of dialog state tracking with the recurrent neural network for encoding important aspects of the dialog history. We describe a two-step gradient descent algorithm that optimizes the tracker with a complex loss function. We demonstrate that this approach yields a dialog state tracker that performs competitively with top-performing trackers participated in the first and second Dialog State Tracking Challenges.

scholarly journals Modulation of spatial and temporal modules in lower limb muscle activations during walking with simulated reduced gravity

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shota Hagio ◽  
Makoto Nakazato ◽  
Motoki Kouzaki
Keyword(s):  
Matrix Factorization ◽  
Lower Limb ◽  
Nonnegative Matrix Factorization ◽  
Walking Speed ◽  
Nonnegative Matrix ◽  
Tensor Decomposition ◽  
Reduced Gravity ◽  
Dependent Variables ◽  
Specific Speed ◽  
Muscle Activations

AbstractGravity plays a crucial role in shaping patterned locomotor output to maintain dynamic stability during locomotion. The present study aimed to clarify the gravity-dependent regulation of modules that organize multiple muscle activities during walking in humans. Participants walked on a treadmill at seven speeds (1–6 km h−1 and a subject- and gravity-specific speed determined by the Froude number (Fr) corresponding to 0.25) while their body weight was partially supported by a lift to simulate walking with five levels of gravity conditions from 0.07 to 1 g. Modules, i.e., muscle-weighting vectors (spatial modules) and phase-dependent activation coefficients (temporal modules), were extracted from 12 lower-limb electromyographic (EMG) activities in each gravity (Fr ~ 0.25) using nonnegative matrix factorization. Additionally, a tensor decomposition model was fit to the EMG data to quantify variables depending on the gravity conditions and walking speed with prescribed spatial and temporal modules. The results demonstrated that muscle activity could be explained by four modules from 1 to 0.16 g and three modules at 0.07 g, and the modules were shared for both spatial and temporal components among the gravity conditions. The task-dependent variables of the modules acting on the supporting phase linearly decreased with decreasing gravity, whereas that of the module contributing to activation prior to foot contact showed nonlinear U-shaped modulation. Moreover, the profiles of the gravity-dependent modulation changed as a function of walking speed. In conclusion, reduced gravity walking was achieved by regulating the contribution of prescribed spatial and temporal coordination in muscle activities.

Knowledge-based Dialog State Tracking

2014 ◽  
Author(s):  
Rudolf Kadlec ◽  
Miroslav Vodolan ◽  
Jindrich Libovicky ◽  
Jan Macek ◽  
Jan Kleindienst
Keyword(s):  
Knowledge Based ◽  
State Tracking ◽  
Dialog State Tracking

The Fourth Dialog State Tracking Challenge

2016 ◽  
pp. 435-449 ◽  
Author(s):  
Seokhwan Kim ◽  
Luis Fernando D’Haro ◽  
Rafael E. Banchs ◽  
Jason D. Williams ◽  
Matthew Henderson
Keyword(s):  
State Tracking ◽  
Dialog State Tracking

scholarly journals Hierarchical Aggregation for Reputation Feedback of Services Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Rong Yang ◽  
Dianhua Wang
Keyword(s):  
Matrix Factorization ◽  
State Of The Art ◽  
The State ◽  
Rating Systems ◽  
Aggregation Model ◽  
Aggregate Score ◽  
Hierarchical Aggregation ◽  
Online Marketplaces ◽  
Online Retailers ◽  
Feature Based

Product ratings are popular tools to support buying decisions of consumers, which are also valuable for online retailers. In online marketplaces, vendors can use rating systems to build trust and reputation. To build trust, it is really important to evaluate the aggregate score for an item or a service. An accurate aggregation of ratings can embody the true quality of offerings, which is not only beneficial for providers in adjusting operation and sales tactics, but also helpful for consumers in discovery and purchase decisions. In this paper, we propose a hierarchical aggregation model for reputation feedback, where the state-of-the-art feature-based matrix factorization models are used. We first present our motivation. Then, we propose feature-based matrix factorization models. Finally, we address how to utilize the above modes to formulate the hierarchical aggregation model. Through a set of experiments, we can get that the aggregate score calculated by our model is greater than the corresponding value obtained by the state-of-the-art IRURe; i.e., the outputs of our models can better match the true rank orders.

Dialog state tracking with attention-based sequence-to-sequence learning

2016 ◽  
Author(s):  
Takaaki Hori ◽  
Hai Wang ◽  
Chiori Hori ◽  
Shinji Watanabe ◽  
Bret Harsham ◽  
...  
Keyword(s):  
Sequence Learning ◽  
State Tracking ◽  
Dialog State Tracking

scholarly journals The Dialog State Tracking Challenge Series

AI Magazine ◽  
2014 ◽  
Vol 35 (4) ◽  
pp. 121-124 ◽  
Author(s):  
Jason D. Williams ◽  
Matthew Henderson ◽  
Antoine Raux ◽  
Blaise Thomson ◽  
Alan Black ◽  
...  
Keyword(s):  
Research Community ◽  
Spoken Dialog Systems ◽  
Dialog Systems ◽  
New Methods ◽  
State Tracking ◽  
Dialog State Tracking

In spoken dialog systems, dialog state tracking refers to the task of correctly inferring the user's goal at a given turn, given all of the dialog history up to that turn. The Dialog State Tracking Challenge is a research community challenge task that has run for three rounds. The challenge has given rise to a host of new methods for dialog state tracking, and also deeper understandings about the problem itself, including methods for evaluation.

Sign in / Sign up

Export Citation Format

Share Document