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Given many recent advanced embedding models, selecting pre-trained wordembedding (a.k.a., word representation) models best fit for a specificdownstream task is non-trivial. In this paper, we propose a systematicapproach, called ETNLP, for extracting, evaluating, and visualizing multiplesets of pre-trained word embeddings to determine which embeddings should beused in a downstream task. For extraction, we provide a method to extractsubsets of the embeddings to be used in the downstream task. For evaluation, weanalyse the quality of pre-trained embeddings using an input word analogy list.Finally, we visualize the word representations in the embedding space toexplore the embedded words interactively.  We demonstrate the effectiveness of the proposed approach on our pre-trainedword embedding models in Vietnamese to select which models are suitable for anamed entity recognition (NER) task. Specifically, we create a large Vietnameseword analogy list to evaluate and select the pre-trained embedding models forthe task. We then utilize the selected embeddings for the NER task and achievethe new state-of-the-art results on the task benchmark dataset. We also applythe approach to another downstream task of privacy-guaranteed embeddingselection, and show that it helps users quickly select the most suitableembeddings. In addition, we create an open-source system using the proposedsystematic approach to facilitate similar studies on other NLP tasks. Thesource code and data are available at https://github.com/vietnlp/etnlp.

ETNLP: a visual-aided systematic approach to select pre-trained embeddings for a downstream task