135 research outputs found
Structured Dialogue State Management for Task-Oriented Dialogue Systems
Human-machine conversational agents have developed at a rapid pace in recent years, bolstered through the application of advanced technologies such as deep learning. Today, dialogue systems are useful in assisting users in various activities, especially task-oriented dialogue systems in specific dialogue domains. However, they continue to be limited in many ways. Arguably the biggest challenge lies in the complexity of natural language and interpersonal communication, and the lack of human context and knowledge available to these systems. This leads to the question of whether dialogue systems, and in particular task-oriented dialogue systems, can be enhanced to leverage various language properties. This work focuses on the semantic structural properties of language in task-oriented dialogue systems. These structural properties are manifest by variable dependencies in dialogue domains; and the study of and accounting for these variables and their interdependencies is the main objective of this research.
Contemporary task-oriented dialogue systems are typically developed with a multiple component architecture, where each component is responsible for a specific process in the conversational interaction. It is commonly accepted that the ability to understand user input in a conversational context, a responsibility generally assigned to the dialogue state tracking component, contributes a huge part to the overall performance of dialogue systems. The output of the dialogue state tracking component, so-called dialogue states, are a representation of the aspects of a dialogue relevant to the completion of a task up to that point, and should also capture the task structural properties of natural language. Here, in a dialogue context dialogue state variables are expressed through dialogue slots and slot values, hence the dialogue state variable dependencies are expressed as the dependencies between dialogue slots and their values. Incorporating slot dependencies in the dialogue state tracking process is herein hypothesised to enhance the accuracy of postulated dialogue states, and subsequently potentially improve the performance of task-oriented dialogue systems.
Given this overall goal and approach to the improvement of dialogue systems, the work in this dissertation can be broken down into two related contributions: (i) a study of structural properties in dialogue states; and (ii) the investigation of novel modelling approaches to capture slot dependencies in dialogue domains.
The analysis of language\u27s structural properties was conducted with a corpus-based study to investigate whether variable dependencies, i.e., slot dependencies when using dialogue system terminology, exist in dialogue domains, and if yes, to what extent do these dependencies affect the dialogue state tracking process. A number of public dialogue corpora were chosen for analysis with a collection of statistical methods being applied to their analysis.
Deep learning architectures have been shown in various works to be an effective method to model conversations and different types of machine learning challenges. In this research, in order to account for slot dependencies, a number of deep learning-based models were experimented with for the dialogue state tracking task. In particular, a multi-task learning system was developed to study the leveraging of common features and shared knowledge in the training of dialogue state tracking subtasks such as tracking different slots, hence investigating the associations between these slots. Beyond that, a structured prediction method, based on energy-based learning, was also applied to account for explicit dialogue slot dependencies.
The study results show promising directions for solving the dialogue state tracking challenge for task-oriented dialogue systems. By accounting for slot dependencies in dialogue domains, dialogue states were produced more accurately when benchmarked against comparative modelling methods that do not take advantage of the same principle. Furthermore, the structured prediction method is applicable to various state-of-the-art modelling approaches for further study.
In the long term, the study of dialogue state slot dependencies can potentially be expanded to a wider range of conversational aspects such as personality, preferences, and modalities, as well as user intents
Costs and Benefits of a Greener Alternative for the Development of Vietnam's power sector
International audienceIn this study, BAU (a scenario based on current trends) and ALT (a greener alternative with more renewables, higher energy efficiency) are developed. The external costs of CO 2 , NOx, SO 2 and PM 10 in the Vietnamese power sector are estimated at 20, 1328, 2047 and 1460 US compared with 974 billion US); lower local pollution costs (73 vs 137 billion US). The outcomes of ALT are in accord with the targets in the most recent Green Growth Strategy of Vietnam
Perspective of CO2 capture & storage (CCS) development in Vietnam: Results from expert interviews
International audienceThis paper summarizes expert opinions regarding crucial factors that mayinfluence Vietnam’s future use of carbon capture and storage (CCS) based onface-to-face interviews in December 2013 with 16 CCS-related experts fromthe Vietnamese government, research institutes, universities and the energyindustrial sector. This study finds that financial incentives and climate policyare the most important factors for the development of CCS technologies inVietnam in the next two decades. Financial incentives involve direct subsidiesfrom the government, such as tax exemptions for land use and the importationof CCS-related equipment. In addition, all the experts agree that internationalfinancial support is important to initiate a large deployment of CCStechnologies in Vietnam by implementing demonstrative/pilot projects to proveCCS’s working efficiency
Low Carbon Scenario for the Power Sector of Vietnam: Externality and Comparison Approach
International audienceIn this paper, BAU (a scenario based on current trends) and ALT (a greener alternative with more renewables, higher energy efficiency) are developed. The external costs of CO2, NOx, SO2 and PM10 in the Vietnamese power sector are estimated at 20, 1328, 2047 and 1460 US compared with 974 billion US); lower local pollution costs (73 vs 137 billion US). The outcomes of ALT are in accord with the targets in the most recent Green Growth Strategy of Vietnam and the Intended Nationally Determined Contributions (INDCs) of the country to UNFCCC and COP21
Incremental Joint Modelling for Dialogue State Tracking
Dialogue State Tracking is an important task in dialogue management as it provides a mechanism to monitor dialogue contributions. In this paper we introduce an Incremental Joint Model as a new approach to the task. Our tracker is capable of incrementally tracking Dialogue States. We base our model and analysis on the datasets provided in the Second Dialogue State Tracking Challenge (DSTC2). Our early stage evaluations are based on comparisons of our tracker with both the baseline model provide by the DSTC2 and also LecTrack: a state-of-the-art incremental LSTM-based tracker. The main finding of our experiments is that moving from an utterance based to incremental word based tracker results in better performance for our RNN based joint task models
Energy-based Neural Modelling for Large-Scale Multiple Domain Dialogue State Tracking
Scaling up dialogue state tracking to multiple domains is challenging due to the growth in the number of variables being tracked. Furthermore, dialog state tracking models do not yet explicitly make use of relationships between dialogue variables, such as slots across domains. We propose using energy-based structure prediction methods for large-scale dialogue state tracking task in two multiple domain dialogue datasets. Our results indicate that: (i) modelling variable dependencies yields better results; and (ii) the structured prediction output aligns with the dialogue slot-value constraint principles. This leads to promising directions to improve state-of-the-art models by incorporating variable dependencies into their prediction process
Energy-Based Modelling for Dialogue State Tracking
The uncertainties of language and the complexity of dialogue contexts make accurate dialogue state tracking one of the more challenging aspects of dialogue processing. To improve state tracking quality, we argue that relationships between different aspects of dialogue state must be taken into account as they can often guide a more accurate interpretation process. To this end, we present an energy-based approach to dialogue state tracking as a structured classification task. The novelty of our approach lies in the use of an energy network on top of a deep learning architecture to explore more signal correlations between network variables including input features and output labels. We demonstrate that the energy-based approach improves the performance of a deep learning dialogue state tracker towards state-of-the-art results without the need for many of the other steps required by current state-of-the-art methods
A Multi-Task Approach to Incremental Dialogue State Tracking
Incrementality is a fundamental feature of language in real world use. To this point, however, the vast majority of work in automated dialogue processing has focused on language as turn based. In this paper we explore the challenge of incremental dialogue state tracking through the development and analysis of a multi-task approach to incremental dialogue state tracking. We present the design of our incremental dialogue state tracker in detail and provide evaluation against the well known Dialogue State Tracking Challenge 2 (DSTC2) dataset. In addition to a standard evaluation of the tracker, we also provide an analysis of the Incrementality phenomenon in our model’s performance by analyzing how early our models can produce correct predictions and how stable those predictions are. We find that the Multi-Task Learning-based model achieves state-of-the-art results for incremental processing
Investigating Variable Dependencies in Dialogue States
Dialogue State Tracking is arguably one of the most challenging tasks among dialogue processing problems due to the uncertainties of language and complexity of dialogue contexts. We argue that this problem is made more challenging by variable dependencies in the dialogue states that must be accounted for in processing. In this paper we give details on our motivation for this argument through statistical tests on a number of dialogue datasets. We also propose a machine learning-based approach called energy-based learning that tackles variable dependencies while performing prediction on the dialogue state tracking tasks
Capturing Dialogue State Variable Dependencies with an Energy-based Neural Dialogue State Tracker
Dialogue state tracking requires the population and maintenance of a multi-slot frame representation of the dialogue state. Frequently, dialogue state tracking systems assume independence between slot values within a frame. In this paper we argue that treating the prediction of each slot value as an independent prediction task may ignore important associations between the slot values, and, consequently, we argue that treating dialogue state tracking as a structured prediction problem can help to improve dialogue state tracking performance. To support this argument, the research presented in this paper is structured into three stages: (i) analyzing variable dependencies in dialogue data; (ii) applying an energy-based methodology to model dialogue state tracking as a structured prediction task; and (iii) evaluating the impact of inter-slot relationships on model performance. Overall, we demonstrate that modelling the associations between target slots with an energy-based formalism improves dialogue state tracking performance in a number of ways
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