Recent trends suggest that neuralnetwork-inspired word embedding models outperform traditional count-based distributional models on word similarity and analogy detection tasks. We reveal that much of the performance gains of word embeddings are due to certain system design choices and hyperparameter optimizations, rather than the embedding algorithms themselves. Furthermore, we show that these modifications can be transferred to traditional distributional models, yielding similar gains. In contrast to prior reports, we observe mostly local or insignificant performance differences between the methods, with no global advantage to any single approach over the others.
However, their claim was challenged by REF , who showed that superiority of neural word embeddings is not due to the embedding algorithm, but due to certain design choices and hyperparameters optimizations.
Improving Distributional Similarity with Lessons Learned from Word Embeddings
Transactions of the Association for Computational Linguistics
Transactions of the Association for Computational Linguistics
Computer Science