Quality Evaluation of C-E Translation of Legal Texts by Mainstream Machine Translation Systems—An Example of DeepL and Metasota

Despite significant progress made in machine translation technology and the ongoing efforts in practical and commercial application of neural machine translation systems, their performance in vertical fields remains unsatisfactory. To avoid misunderstandings and excessive expectations of a specific machine translation system, this research selected legal texts as its real data research object. The text translation tasks were accomplished using two popular neural machine translation systems, DeepL and Metasota, both domestically and internationally, and evaluated using internationally recognized BLEU algorithm to reflect their Chinese-to-English translation performance in legal fields. Based on the determined BLEU score, the study adopted an artificial analysis method to analyze the grammatical aspects of the machine translation output, including the accuracy of terminology usage, word order, subject-verb agreement, sentence structure, tense, and voice to enable readers to have a rational understanding of the gap between machine translation and human translation in legal text translation, and objectively assess the application and future development prospects of machine translation in legal text fields. The experimental results indicate that machine translation systems still face challenges in achieving high-quality legal text translations and meeting practical needs, and that further post-translation editing research is needed to improve the accuracy of legal text translation

The Determinants of IS User Satisfaction and Dissatisfaction: A Text Mining Approach

Too often, in previous marketing, consumer behavior, and IS research, satisfaction and dissatisfaction are treated as two ends of a bipolar continuum. The researchers of this study argue that satisfaction and dissatisfaction are two distinct dimensions and thus have different determinants. Online reviews, as one type of user-generated contents (UGC), can impact consumer purchase decision and IS user adoption decision. Online reviews are also valuable sources for researchers and practitioners to better understand consumers and users. The researchers of this study extract and analyze online user reviews in the App Store. Sentiment analysis is applied to model user satisfaction and dissatisfaction. Significant determinants, as well as their weights are identified. By using the text mining techniques, the current study demonstrates the separability of satisfaction and dissatisfaction and reveals different influencing factors. The research findings can provide insights into extant IS user satisfaction literature

Deep reinforcement learning for multi-domain dialogue systems

Standard deep reinforcement learning methods such as Deep Q-Networks (DQN) for multiple tasks (domains) face scalability problems. We propose a method for multi-domain dialogue policy learning---termed NDQN, and apply it to an information-seeking spoken dialogue system in the domains of restaurants and hotels. Experimental results comparing DQN (baseline) versus NDQN (proposed) using simulations report that our proposed method exhibits better scalability and is promising for optimising the behaviour of multi-domain dialogue systems

Scaling up deep reinforcement learning for multi-domain dialogue systems

Standard deep reinforcement learning methods such as Deep Q-Networks (DQN) for multiple tasks (domains) face scalability problems due to large search spaces. This paper proposes a three-stage method for multi-domain dialogue policy learning—termed NDQN, and applies it to an information-seeking spoken dialogue system in the domains of restaurants and hotels. In this method, the first stage does multi-policy learning via a network of DQN agents; the second makes use of compact state representations by compressing raw inputs; and the third stage applies a pre-training phase for bootstraping the behaviour of agents in the network. Experimental results comparing DQN (baseline) versus NDQN (proposed) using simulations report that the proposed method exhibits better scalability and is promising for optimising the behaviour of multi-domain dialogue systems. An additional evaluation reports that the NDQN agents outperformed a K-Nearest Neighbour baseline in task success and dialogue length, yielding more efficient and successful dialogues

Attracting Investment to REDD+: Capitalizing on Co-Benefits?

At its inception in 2007, the United Nations-sponsored Reducing Emissions from Deforestation and Forest Degradation (REDD+) mechanism had one primary goal: to mitigate carbon dioxide emissions from the global forest sector, which currently account for approximately 10% of global carbon emissions. REDD+ has undergone various modifications to its scope and approach in the succeeding nine years, but little has yet come from subsequent UN climate negotiations in the way of creating an obligatory financing scheme that would require participation from actors in developed countries. Today, dozens of preliminary REDD+ projects are operational across the world, but these projects receive strictly voluntary funding from a suite of public and private actors, including national governments and companies engaged in social responsibility practices. Despite some successes in this voluntary realm and promises of REDD+ advancement at recent negotiations, it has become clear that without assured funding – and pending an international financing mechanism for REDD+ – projects face an increasingly difficult environment for attaining capital resources. Scaling up the mechanism will be virtually impossible without addressing the imbalance between supply and demand for REDD+ credits in the voluntary stage. Code REDD, a San Francisco-based non-governmental organization whose mission is to support and scale the REDD+ mechanism, is attempting to discover whether untapped opportunities exist for sustaining REDD+ before the commencement of an international financing scheme, specifically by capitalizing on the co-benefits of REDD+ projects: the social and environmental outcomes that inherently accompany responsibly designed carbon offset projects. These co-benefits can include biodiversity benefits, freshwater provision, community economic development, and women’s empowerment. This question of the potential for co-benefit quantification and sale as a means to sustain REDD+ in the voluntary phase was the foundation of the research we undertook here. We aimed to determine how REDD+ stakeholders envisioned the role of co-benefits within the financing of REDD+, and if further efforts to quantify and sell them could bear meaningful results for the future of the mechanism. Splitting the REDD+ community into two distinct categories – practitioners (those who design, implement, and monitor REDD+ projects) and investors (both those who purchase REDD+ credits and those who invest in REDD+ projects) – we held more than twenty interviews to determine the answer to the above question. We found that, though co-benefits were considered an important – even indispensable – part of REDD+ success, few practitioners or investors were interested in their further quantification or expected that voluntary REDD+ could be sustained based on such action. That said, many current and potential investors offered insight into how the business case for REDD+ could be better articulated in order to attract more investment. Also, in speaking with practitioners, we identified ways that the mechanism could be better integrated with other contemporary environmental efforts, including biodiversity offsetting and water funds, offering what we believe could represent partial solutions to the REDD+ demand shortfall

Memantine Improves Attentional Processes in Fragile X-Associated Tremor/Ataxia Syndrome: Electrophysiological Evidence from a Randomized Controlled Trial.

Progressive cognitive deficits are common in patients with fragile X-associated tremor/ataxia syndrome (FXTAS), with no targeted treatment yet established. In this substudy of the first randomized controlled trial for FXTAS, we examined the effects of NMDA antagonist memantine on attention and working memory. Data were analyzed for patients (24 in each arm) who completed both the primary memantine trial and two EEG recordings (at baseline and follow-up) using an auditory "oddball" task. Results demonstrated significantly improved attention/working memory performance after one year only for the memantine group. The event-related potential P2 amplitude elicited by non-targets was significantly enhanced in the treated group, indicating memantine-associated improvement in attentional processes at the stimulus identification/discrimination level. P2 amplitude increase was positively correlated with improvement on the behavioral measure of attention/working memory during target detection. Analysis also revealed that memantine treatment normalized the P2 habituation effect at the follow-up visit. These findings indicate that memantine may benefit attentional processes that represent fundamental components of executive function/dysfunction, thought to comprise the core cognitive deficit in FXTAS. The results provide evidence of target engagement of memantine, as well as therapeutically relevant information that could further the development of specific cognitive or disease-modifying therapies for FXTAS

Association between eczema and major cardiovascular outcomes in population-based studies: a systematic review protocol.

INTRODUCTION: Chronic inflammatory diseases such as eczema (also known as atopic dermatitis) have been inconsistently linked to cardiovascular disease and stroke in both mechanistic and epidemiological studies. There is a need to review the existing epidemiological data examining the association between eczema and major cardiovascular outcomes, including angina, myocardial infarction, coronary revascularisation, heart failure, cardiac arrhythmias, stroke and cardiovascular death, in order to improve our understanding of the comorbidities of eczema. METHODS AND ANALYSIS: We will systematically review population-based studies, including cohort, case-control and cross-sectional studies, reporting on the association between eczema and cardiovascular outcomes. We will search Medline, Embase and Global Health, from their date of inception to April 2017, using a comprehensive search strategy formulated with the help of a librarian. Two reviewers will independently screen titles and abstracts in duplicate, followed by independent data extraction and quality assessment. We will group studies by the cardiovascular outcome under study and synthesise them narratively. If sufficient numbers of homogeneous studies are returned, we will perform meta-analyses to obtain pooled effect estimates. Preferred Reporting Items for Systematic Review and Meta-Analysis will be used to inform the reporting of this study. TRIAL REGISTRATION NUMBER: CRD42017060359

Hardness of Braided Quantum Circuit Optimization in the Surface Code

Large-scale quantum information processing requires the use of quantum error-correcting codes to mitigate the effects of noise in quantum devices. Topological error-correcting codes, such as surface codes, are promising candidates, as they can be implemented using only local interactions in a 2-D array of physical qubits. Procedures, such as defect braiding and lattice surgery, can then be used to realize a fault-tolerant universal set of gates on the logical space of such topological codes. However, error correction also introduces a significant overhead in computation time, the number of physical qubits, and the number of physical gates. While optimizing fault-tolerant circuits to minimize this overhead is critical, the computational complexity of such optimization problems remains unknown. This ambiguity leaves room for doubt surrounding the most effective methods for compiling fault-tolerant circuits for a large-scale quantum computer. In this article, we show that the optimization of a special subset of braided quantum circuits is NP-hard by a polynomial-time reduction of the optimization problem into a specific problem called PlanarRectilinear3SAT.journal articl

Hardness of braided quantum circuit optimization in the surface code

Large-scale quantum information processing requires the use of quantum error correcting codes to mitigate the effects of noise in quantum devices. Topological error-correcting codes, such as surface codes, are promising candidates as they can be implemented using only local interactions in a two-dimensional array of physical qubits. Procedures such as defect braiding and lattice surgery can then be used to realize a fault-tolerant universal set of gates on the logical space of such topological codes. However, error correction also introduces a significant overhead in computation time, the number of physical qubits, and the number of physical gates. While optimizing fault-tolerant circuits to minimize this overhead is critical, the computational complexity of such optimization problems remains unknown. This ambiguity leaves room for doubt surrounding the most effective methods for compiling fault-tolerant circuits for a large-scale quantum computer. In this paper, we show that the optimization of a special subset of braided quantum circuits is NP-hard by a polynomial-time reduction of the optimization problem into a specific problem called Planar Rectilinear 3SAT.Comment: 9 pages, 9 figure