Visualization-based improvement of neural machine translation

Munz, Tanja; Väth, Dirk; Kuznecov, Paul; Vu, Ngoc Thang; Weiskopf, Daniel

doi:10.1016/j.cag.2021.12.003

Cited by 6 publications

(2 citation statements)

References 22 publications

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“…For example, with the Chinese Room system MT practitioners can interactively decode and correct translations by visualizing the source and translation tokens side by side [1]. Similarly, NMTVis [78], SoftAlignments [105], and NeuralMonkey [41] use interactive visualization techniques such as parallel coordinate plots, node-link diagrams, and heatmaps to help MT practitioners analyze attention weights and verify translation results. MT researchers also use visual analytics tools [e.g., 56,80,131] to better understand MT evaluation metrics such as BLEU, ChrF, and METEOR scores.…”

Section: Visualization Tools For Evaluation In Machine Translationmentioning

confidence: 99%

Angler: Helping Machine Translation Practitioners Prioritize Model Improvements

Robertson

Wang

Moritz

et al. 2023

Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems

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Angler enables ML developers to easily explore and curate challenge sets for machine translation. (A) The Table View lists all challenge sets, allowing users to compare them by metrics such as sample count, model performance, and familiarity score. After selecting a set, (B) the Detail View allows users to further explore samples in this set across various dimensions. (B1) The Timeline enables users to query data samples by time. (B2) The Spotlight presents visualizations with linking and brushing to help users characterize the set from different angles. (B3) The Sentence List shows all selected data samples and allows users to further fine-tune before exporting this challenge set for downstream tasks.

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Section: Visualization Tools For Evaluation In Machine Translationmentioning

confidence: 99%

Angler: Helping Machine Translation Practitioners Prioritize Model Improvements

Robertson

Wang

Moritz

et al. 2023

Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems

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“…Recently several DL algorithms have been proposed [6]. These algorithms have been widely used to improve many tasks, such as keyphrase extraction [7], machine translation [8], sentiment analysis [9], question-answer systems [10], words recognition system [11], and recommender system [12]. In contrast, we found that most of the reviews that focused on the use of DL algorithms did not discuss at length their use of the keyphrase extraction task, but rather on the basis of a set of tasks [6], [13].…”

Section: Introductionmentioning

confidence: 99%

Automatic keyphrases extraction: an overview of deep learning approaches

Ajallouda

Fagroud²,

Zellou³

et al. 2023

Bulletin EEI

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Automatic keyphrases extraction (AKE) is a principal task in natural language processing (NLP). Several techniques have been exploited to improve the process of extracting keyphrases from documents. Deep learning (DL) algorithms are the latest techniques used in prediction and extraction of keyphrases. DL is one of the most complex types of machine learning, relying on the use of artificial neural networks to make the machine follow the same decision-making path as the human brain. In this paper, we present a review of deep learning-based methods for AKE from documents, to highlight their contribution to improving keyphrase extraction performance. This review will also provide researchers with a collection of data and information on the mechanisms of deep learning algorithms in the AKE domain. This will allow them to solve problems encountered by AKE approaches and propose new methods for improving key-extraction performance.

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