scholarly journals Recent Advances in Dialogue Machine Translation

Information ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 484
Author(s):  
Siyou Liu ◽  
Yuqi Sun ◽  
Longyue Wang
Keyword(s):  
Personality Traits ◽  
Machine Translation ◽  
State Of The Art ◽  
The State ◽  
Comprehensive Review ◽  
Survey Paper ◽  
Recent Advances ◽  
Art Performance ◽  
Hotel Booking ◽  
Significant Application

Recent years have seen a surge of interest in dialogue translation, which is a significant application task for machine translation (MT) technology. However, this has so far not been extensively explored due to its inherent characteristics including data limitation, discourse properties and personality traits. In this article, we give the first comprehensive review of dialogue MT, including well-defined problems (e.g., 4 perspectives), collected resources (e.g., 5 language pairs and 4 sub-domains), representative approaches (e.g., architecture, discourse phenomena and personality) and useful applications (e.g., hotel-booking chat system). After systematical investigation, we also build a state-of-the-art dialogue NMT system by leveraging a breadth of established approaches such as novel architectures, popular pre-training and advanced techniques. Encouragingly, we push the state-of-the-art performance up to 62.7 BLEU points on a commonly-used benchmark by using mBART pre-training. We hope that this survey paper could significantly promote the research in dialogue MT.

scholarly journals Relation-Guided Spatial Attention and Temporal Refinement for Video-Based Person Re-Identification

2020 ◽  
Vol 34 (07) ◽  
pp. 11434-11441
Author(s):  
Xingze Li ◽  
Wengang Zhou ◽  
Yun Zhou ◽  
Houqiang Li
Keyword(s):  
Spatial Attention ◽  
Video Surveillance ◽  
State Of The Art ◽  
The State ◽  
Temporal Information ◽  
Feature Representations ◽  
Art Performance ◽  
The Individual ◽  
Significant Application

Video-based person re-identification has received considerable attention in recent years due to its significant application in video surveillance. Compared with image-based person re-identification, video-based person re-identification is characterized by a much richer context, which raises the significance of identifying informative regions and fusing the temporal information across frames. In this paper, we propose two relation-guided modules to learn reinforced feature representations for effective re-identification. First, a relation-guided spatial attention (RGSA) module is designed to explore the discriminative regions globally. The weight at each position is determined by its feature as well as the relation features from other positions, revealing the dependence between local and global contents. Based on the adaptively weighted frame-level feature, then, a relation-guided temporal refinement (RGTR) module is proposed to further refine the feature representations across frames. The learned relation information via the RGTR module enables the individual frames to complement each other in an aggregation manner, leading to robust video-level feature representations. Extensive experiments on four prevalent benchmarks verify the state-of-the-art performance of the proposed method.

scholarly journals Recent advances in understanding oxygen evolution reaction mechanisms over iridium oxide

2021 ◽  
Author(s):  
Takahiro Naito ◽  
Tatsuya Shinagawa ◽  
Takeshi Nishimoto ◽  
Kazuhiro Takanabe
Keyword(s):  
Reaction Mechanism ◽  
Oxygen Evolution ◽  
Oxygen Evolution Reaction ◽  
Reaction Mechanisms ◽  
State Of The Art ◽  
Iridium Oxide ◽  
The State ◽  
Computational Studies ◽  
Recent Advances ◽  
Iridium Oxides

Recent spectroscopic and computational studies concerning the oxygen evolution reaction over iridium oxides are reviewed to provide the state-of-the-art understanding of its reaction mechanism.

scholarly journals A Survey of Robots in Healthcare

Technologies ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 8 ◽  
Author(s):  
Maria Kyrarini ◽  
Fotios Lygerakis ◽  
Akilesh Rajavenkatanarayanan ◽  
Christos Sevastopoulos ◽  
Harish Ram Nambiappan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
State Of The Art ◽  
The Elderly ◽  
Robotic Systems ◽  
Care Hospital ◽  
Robotic Technology ◽  
Persons With Disabilities ◽  
Survey Paper ◽  
Recent Advances ◽  
Art Research

In recent years, with the current advancements in Robotics and Artificial Intelligence (AI), robots have the potential to support the field of healthcare. Robotic systems are often introduced in the care of the elderly, children, and persons with disabilities, in hospitals, in rehabilitation and walking assistance, and other healthcare situations. In this survey paper, the recent advances in robotic technology applied in the healthcare domain are discussed. The paper provides detailed information about state-of-the-art research in care, hospital, assistive, rehabilitation, and walking assisting robots. The paper also discusses the open challenges healthcare robots face to be integrated into our society.

Lexicography

2018 ◽  
Author(s):  
Seyed Mostafa Assi
Keyword(s):  
Information Technology ◽  
State Of The Art ◽  
Applied Linguistics ◽  
The State ◽  
Western Countries ◽  
Persian Language ◽  
Recent Advances ◽  
History Of ◽  
Middle Persian ◽  
Modern Era

The history of lexicography in Iran dates back to more than 2,000 years ago, to the time of the compilation of bilingual and monolingual lexicons for the Middle Persian language. After a review of the long and rich tradition of Persian lexicography, the chapter gives an account of the state of the art in the modern era by describing recent advances and developments in this field. During the last three or four decades, in line with the advancements in western countries, Iranian lexicography evolved from its traditional state into a modern professional and academic activity trying to improve the form and content of dictionaries by implementing the following factors: the latest achievements in theoretical and applied linguistics related to lexicography; and the computer techniques and information technology and corpus-based approach to lexicography.

scholarly journals A Graph-based Evolutionary Algorithm for Automated Machine Learning

2020 ◽  
Author(s):  
Fei Qi ◽  
Zhaohui Xia ◽  
Gaoyang Tang ◽  
Hang Yang ◽  
Yu Song ◽  
...  
Keyword(s):  
Machine Learning ◽  
Evolutionary Algorithm ◽  
Parameter Optimization ◽  
State Of The Art ◽  
The State ◽  
Complex Structures ◽  
Architecture Evolution ◽  
Automated Machine Learning ◽  
Art Performance

As an emerging field, Automated Machine Learning (AutoML) aims to reduce or eliminate manual operations that require expertise in machine learning. In this paper, a graph-based architecture is employed to represent flexible combinations of ML models, which provides a large searching space compared to tree-based and stacking-based architectures. Based on this, an evolutionary algorithm is proposed to search for the best architecture, where the mutation and heredity operators are the key for architecture evolution. With Bayesian hyper-parameter optimization, the proposed approach can automate the workflow of machine learning. On the PMLB dataset, the proposed approach shows the state-of-the-art performance compared with TPOT, Autostacker, and auto-sklearn. Some of the optimized models are with complex structures which are difficult to obtain in manual design.

scholarly journals Comparative Quality Estimation for Machine Translation Observations on Machine Learning and Features

2017 ◽  
Vol 108 (1) ◽  
pp. 307-318 ◽  
Author(s):  
Eleftherios Avramidis
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Machine Translation ◽  
State Of The Art ◽  
Linear Method ◽  
The State ◽  
Quality Estimation ◽  
Art Methods ◽  
Improved Performance

AbstractA deeper analysis on Comparative Quality Estimation is presented by extending the state-of-the-art methods with adequacy and grammatical features from other Quality Estimation tasks. The previously used linear method, unable to cope with the augmented features, is replaced with a boosting classifier assisted by feature selection. The methods indicated show improved performance for 6 language pairs, when applied on the output from MT systems developed over 7 years. The improved models compete better with reference-aware metrics.Notable conclusions are reached through the examination of the contribution of the features in the models, whereas it is possible to identify common MT errors that are captured by the features. Many grammatical/fluency features have a good contribution, few adequacy features have some contribution, whereas source complexity features are of no use. The importance of many fluency and adequacy features is language-specific.

State-of-the-Art in Micro- and Meso-Scale Heat Exchangers

1999 ◽  
Author(s):  
Reza Shekarriz ◽  
Charles J. Call
Keyword(s):  
Heat Exchanger ◽  
Research And Development ◽  
Heat Exchangers ◽  
State Of The Art ◽  
The State ◽  
Manufacturing Processes ◽  
Review Of The Literature ◽  
Design Constraints ◽  
Recent Advances ◽  
Meso Scale

Abstract A review of the literature and the state-of-the-art in research and development of miniature heat exchangers is presented in this paper. The authors provide a discussion of what makes the micro- and meso-scales important, highlight the design constraints and challenges that surface when miniaturizing a heat exchanger, and outline and discuss the outstanding practical and scientific issues in this area. Finally, the most recent advances in manufacturing processes and application of these miniature heat exchangers are covered in this article.

scholarly journals Fast and Simple Mixture of Softmaxes with BPE and Hybrid-LightRNN for Language Generation

2019 ◽  
Vol 33 ◽  
pp. 6626-6633
Author(s):  
Xiang Kong ◽  
Qizhe Xie ◽  
Zihang Dai ◽  
Eduard Hovy
Keyword(s):  
Machine Translation ◽  
State Of The Art ◽  
The State ◽  
Computational Time ◽  
Memory Consumption ◽  
Image Captioning ◽  
Vocabulary Size ◽  
Language Generation ◽  
Practical Applications ◽  
Coding Schemes

Mixture of Softmaxes (MoS) has been shown to be effective at addressing the expressiveness limitation of Softmax-based models. Despite the known advantage, MoS is practically sealed by its large consumption of memory and computational time due to the need of computing multiple Softmaxes. In this work, we set out to unleash the power of MoS in practical applications by investigating improved word coding schemes, which could effectively reduce the vocabulary size and hence relieve the memory and computation burden. We show both BPE and our proposed Hybrid-LightRNN lead to improved encoding mechanisms that can halve the time and memory consumption of MoS without performance losses. With MoS, we achieve an improvement of 1.5 BLEU scores on IWSLT 2014 German-to-English corpus and an improvement of 0.76 CIDEr score on image captioning. Moreover, on the larger WMT 2014 machine translation dataset, our MoSboosted Transformer yields 29.6 BLEU score for English-toGerman and 42.1 BLEU score for English-to-French, outperforming the single-Softmax Transformer by 0.9 and 0.4 BLEU scores respectively and achieving the state-of-the-art result on WMT 2014 English-to-German task.

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