scholarly journals A review of the state-of-the-art in automatic post-editing

Author(s):  
Félix do Carmo ◽  
Dimitar Shterionov ◽  
Joss Moorkens ◽  
Joachim Wagner ◽  
Murhaf Hossari ◽  
...  

AbstractThis article presents a review of the evolution of automatic post-editing, a term that describes methods to improve the output of machine translation systems, based on knowledge extracted from datasets that include post-edited content. The article describes the specificity of automatic post-editing in comparison with other tasks in machine translation, and it discusses how it may function as a complement to them. Particular detail is given in the article to the five-year period that covers the shared tasks presented in WMT conferences (2015–2019). In this period, discussion of automatic post-editing evolved from the definition of its main parameters to an announced demise, associated with the difficulties in improving output obtained by neural methods, which was then followed by renewed interest. The article debates the role and relevance of automatic post-editing, both as an academic endeavour and as a useful application in commercial workflows.

2013 ◽  
Vol 48 ◽  
pp. 733-782 ◽  
Author(s):  
T. Xiao ◽  
J. Zhu

This article presents a probabilistic sub-tree alignment model and its application to tree-to-tree machine translation. Unlike previous work, we do not resort to surface heuristics or expensive annotated data, but instead derive an unsupervised model to infer the syntactic correspondence between two languages. More importantly, the developed model is syntactically-motivated and does not rely on word alignments. As a by-product, our model outputs a sub-tree alignment matrix encoding a large number of diverse alignments between syntactic structures, from which machine translation systems can efficiently extract translation rules that are often filtered out due to the errors in 1-best alignment. Experimental results show that the proposed approach outperforms three state-of-the-art baseline approaches in both alignment accuracy and grammar quality. When applied to machine translation, our approach yields a +1.0 BLEU improvement and a -0.9 TER reduction on the NIST machine translation evaluation corpora. With tree binarization and fuzzy decoding, it even outperforms a state-of-the-art hierarchical phrase-based system.


2017 ◽  
Vol 108 (1) ◽  
pp. 307-318 ◽  
Author(s):  
Eleftherios Avramidis

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.


Author(s):  
Anass Nouri ◽  
Christophe Charrier ◽  
Olivier Lezoray

This chapter concerns the visual saliency and the perceptual quality assessment of 3D meshes. Firstly, the chapter proposes a definition of visual saliency and describes the state-of-the-art methods for its detection on 3D mesh surfaces. A focus is made on a recent model of visual saliency detection for 3D colored and non-colored meshes whose results are compared with a ground-truth saliency as well as with the literature's methods. Since this model is able to estimate the visual saliency on 3D colored meshes, named colorimetric saliency, a description of the construction of a 3D colored mesh database that was used to assess its relevance is presented. The authors also describe three applications of the detailed model that respond to the problems of viewpoint selection, adaptive simplification and adaptive smoothing. Secondly, two perceptual quality assessment metrics for 3D non-colored meshes are described, analyzed, and compared with the state-of-the-art approaches.


2020 ◽  
pp. 15-34
Author(s):  
Bill Ayrey

What is the definition of a space suit? This chapter explains and chronicles the development of the model XMC2-ILC pressure suit for the early U.S. Air Force high-altitude aircraft.


Author(s):  
Xiang Kong ◽  
Qizhe Xie ◽  
Zihang Dai ◽  
Eduard Hovy

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.


2003 ◽  
Vol 23 ◽  
pp. 202-212 ◽  
Author(s):  
John H. McWhorter

The interface between creole studies and language change has been a tumultuous area since the late 1990s. Evidence has been found confirming that children created Hawaiian Creole English; the “decreolization” approach to the creole continuum has become largely obsolete; work on creoles and grammaticalization has expanded beyond its former concentration on Tok Pisin; creolists working within the generative syntax tradition have questioned whether creolization is a distinct process at all; other work argues that creoles are synchronically as well as sociohistorically definable; and the very centrality of plantation contexts' sociology to creole genesis has been questioned. Concepts often taken as assumptions ten years ago are now widely questioned, even the very definition of creole itself.


2020 ◽  
Vol 34 (05) ◽  
pp. 7594-7601
Author(s):  
Pierre Colombo ◽  
Emile Chapuis ◽  
Matteo Manica ◽  
Emmanuel Vignon ◽  
Giovanna Varni ◽  
...  

The task of predicting dialog acts (DA) based on conversational dialog is a key component in the development of conversational agents. Accurately predicting DAs requires a precise modeling of both the conversation and the global tag dependencies. We leverage seq2seq approaches widely adopted in Neural Machine Translation (NMT) to improve the modelling of tag sequentiality. Seq2seq models are known to learn complex global dependencies while currently proposed approaches using linear conditional random fields (CRF) only model local tag dependencies. In this work, we introduce a seq2seq model tailored for DA classification using: a hierarchical encoder, a novel guided attention mechanism and beam search applied to both training and inference. Compared to the state of the art our model does not require handcrafted features and is trained end-to-end. Furthermore, the proposed approach achieves an unmatched accuracy score of 85% on SwDA, and state-of-the-art accuracy score of 91.6% on MRDA.


Author(s):  
Hao Xiong ◽  
Zhongjun He ◽  
Hua Wu ◽  
Haifeng Wang

Discourse coherence plays an important role in the translation of one text. However, the previous reported models most focus on improving performance over individual sentence while ignoring cross-sentence links and dependencies, which affects the coherence of the text. In this paper, we propose to use discourse context and reward to refine the translation quality from the discourse perspective. In particular, we generate the translation of individual sentences at first. Next, we deliberate the preliminary produced translations, and train the model to learn the policy that produces discourse coherent text by a reward teacher. Practical results on multiple discourse test datasets indicate that our model significantly improves the translation quality over the state-of-the-art baseline system by +1.23 BLEU score. Moreover, our model generates more discourse coherent text and obtains +2.2 BLEU improvements when evaluated by discourse metrics.


1980 ◽  
Vol 11 (1) ◽  
pp. 65-76 ◽  
Author(s):  
Virginia C. Little

Need is a multi-faceted, multi-dimensional concept. National and international efforts to assess the needs of the elderly confront common problems: lack of an accepted definition of terms such as “need,” “want” and “demand;” perceptions which vary with age, professional role, relationship and time; a range of methodologies encompassing rational, empirical and relativistic approaches, as well as subjective, objective and statistical measures. Research instruments are being refined and shared, but the state of the art remains in its infancy. Use of index of incapacity measures appears to offer the best data base for planning needed services.


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