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

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$/ton, respectively. The authors find that the electricity price and the domestic trade balance in ALT are less sensitive to fluctuations in the international price of coal than in BAU. The total costs accumulated between period 2010-2040 would be lower in ALT: 632 billion US$ compared with 974 billion US$. This difference arises from several factors: lower investment in new capacity (226 vs 306 billion US$); lower local pollution costs (73 vs 137 billion US$); and lower expenditures on imported fuels (57 vs 115 billion US$). The outcomes of ALT are in accord with the targets in the most recent Green Growth Strategy of Vietnam

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$/ton, respectively. The authors find the electricity price and the domestic trade balance in ALT less sensitive to fluctuations in the international price of coal than in BAU. The total costs accumulated between the period of 2010-2040 would be lower in ALT: 632 billion US$ compared with 974 billion US$. This difference arises from several factors: lower investment in new capacity (226 vs 306 billion US$); lower local pollution costs (73 vs 137 billion US$); and lower expenditures on imported fuels (57 vs 115 billion US$). 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

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

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

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

F-Measure Optimisation and Label Regularisation for Energy-Based Neural Dialogue State Tracking Models

In recent years many multi-label classification methods have exploited label dependencies to improve performance of classification tasks in various domains, hence casting the tasks to structured prediction problems. We argue that multi-label predictions do not always satisfy domain constraint restrictions. For example when the dialogue state tracking task in task-oriented dialogue domains is solved with multi-label classification approaches, slot-value constraint rules should be enforced following real conversation scenarios. To address these issues we propose an energy-based neural model to solve the dialogue state tracking task as a structured prediction problem. Furthermore we propose two improvements over previous methods with respect to dialogue slot-value constraint rules: (i) redefining the estimation conditions for the energy network; (ii) regularising label predictions following the dialogue slot-value constraint rules. In our results we find that our extended energy-based neural dialogue state tracker yields better overall performance in term of prediction accuracy, and also behaves more naturally with respect to the conversational rules