ETNLP: a visual-aided systematic approach to select pre-trained embeddings for a downstream task (1903.04433v2)

Abstract: Given many recent advanced embedding models, selecting pre-trained word embedding (a.k.a., word representation) models best fit for a specific downstream task is non-trivial. In this paper, we propose a systematic approach, called ETNLP, for extracting, evaluating, and visualizing multiple sets of pre-trained word embeddings to determine which embeddings should be used in a downstream task. For extraction, we provide a method to extract subsets of the embeddings to be used in the downstream task. For evaluation, we analyse the quality of pre-trained embeddings using an input word analogy list. Finally, we visualize the word representations in the embedding space to explore the embedded words interactively. We demonstrate the effectiveness of the proposed approach on our pre-trained word embedding models in Vietnamese to select which models are suitable for a named entity recognition (NER) task. Specifically, we create a large Vietnamese word analogy list to evaluate and select the pre-trained embedding models for the task. We then utilize the selected embeddings for the NER task and achieve the new state-of-the-art results on the task benchmark dataset. We also apply the approach to another downstream task of privacy-guaranteed embedding selection, and show that it helps users quickly select the most suitable embeddings. In addition, we create an open-source system using the proposed systematic approach to facilitate similar studies on other NLP tasks. The source code and data are available at https://github.com/vietnlp/etnlp.

• The paper introduces ETNLP, a novel pipeline integrating extraction, evaluation, and visualization to select optimal embeddings for Vietnamese NLP tasks.
• It employs models like fastText and ELMO, achieving state-of-the-art results in NER and robust performance in word analogy tasks measured by MAP.
• The approach offers practical benefits in privacy-guaranteed embedding selection and provides a scalable blueprint for low-resource language applications.

An Examination of ETNLP: A Systematic Pipeline for Vietnamese NLP Tasks

This paper introduces ETNLP, a systematic approach designed to evaluate, extract, and visualize pre-trained word embeddings for use in downstream NLP tasks. The authors focus on determining optimal embeddings through an integrative pipeline methodology, with a specific application to the Vietnamese language. ETNLP is graphically constructed around three main components: Extractor, Evaluator, and Visualizer, thereby providing an end-to-end solution to handle the complexities of embedding selection, especially in non-English languages.

The paper underscores the challenges in selecting effective pre-trained embeddings from the numerous advanced models available. Traditional models like Word2Vec, GloVe, and FastText, while valuable, necessitate context sensitivity enhancement as seen in innovations like ELMO and BERT. The ETNLP pipeline is a comprehensive attempt to tackle the multifaceted task of embedding model evaluation, enabling more informed decisions related to NLP applications. The authors assert that no existing framework fully accomplishes the integration of extraction, evaluation, and visualization as obtained with ETNLP.

A critical component of ETNLP is the Evaluator, tasked with assessing embeddings using a word analogy list created for Vietnamese—a low-resource language that lacks extensive lexical benchmarks. The performance in a Named Entity Recognition (NER) task highlights ETNLP's ability to leverage embeddings to achieve state-of-the-art results, notably utilizing fastText, ELMO, and a concatenated embedding set (MULTI) for superior performance. The empirical paper also featured statistical analyses, focusing on the evaluator's assessment using mean average precision (MAP) on the word analogy task, illustrating significant outcomes of fastText and ELMO models relative to others.

An additional downstream use case explored is privacy-guaranteed embedding selection, showing ETNLP's broader applicability. By examining dpUGC models, the authors demonstrate how the pipeline can facilitate knowledge sharing while balancing privacy concerns with embedding utility transparently.

The implications of this work are twofold. Practically, ETNLP offers a streamlined solution to embedding selection tailored for Vietnamese NLP tasks and potentially adaptable to other languages with similar complexities. Theoretically, it provides a model architecture where embeddings are not only assessed by benchmark tests but are optionally extensible to more advanced diacritic and syntactical modeling. Looking ahead, the authors plan to enhance ETNLP with additional language support and improved visualization capabilities, aligning with ongoing developments in AI and NLP.

In conclusion, ETNLP reflects a significant methodological advancement in NLP, advocating for a more dynamic and utilizable approach to embedding selection. By creating robust resources for Vietnamese and extending its framework, ETNLP provides a valuable blueprint potentially generalizable to a global context of diverse linguistic needs in embedded NLP tasks.