Word2Vec: Optimal Hyper-Parameters and Their Impact on NLP Downstream Tasks

• URL: http://arxiv.org/abs/2003.11645v3
• Date: Sat, 17 Apr 2021 06:02:44 GMT
• Title: Word2Vec: Optimal Hyper-Parameters and Their Impact on NLP Downstream Tasks
• Authors: Tosin P. Adewumi, Foteini Liwicki and Marcus Liwicki
• Abstract summary: We show optimal combination of hyper- parameters exists and evaluate various combinations. We obtain better human-assigned WordSim scores, corresponding Spearman correlation and better downstream performances compared to the original model.
• Score: 1.6507910904669727
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Word2Vec is a prominent model for natural language processing (NLP) tasks. Similar inspiration is found in distributed embeddings for new state-of-the-art (SotA) deep neural networks. However, wrong combination of hyper-parameters can produce poor quality vectors. The objective of this work is to empirically show optimal combination of hyper-parameters exists and evaluate various combinations. We compare them with the released, pre-trained original word2vec model. Both intrinsic and extrinsic (downstream) evaluations, including named entity recognition (NER) and sentiment analysis (SA) were carried out. The downstream tasks reveal that the best model is usually task-specific, high analogy scores don't necessarily correlate positively with F1 scores and the same applies to focus on data alone. Increasing vector dimension size after a point leads to poor quality or performance. If ethical considerations to save time, energy and the environment are made, then reasonably smaller corpora may do just as well or even better in some cases. Besides, using a small corpus, we obtain better human-assigned WordSim scores, corresponding Spearman correlation and better downstream performances (with significance tests) compared to the original model, trained on 100 billion-word corpus.

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