Siamese CBOW: Optimizing Word Embeddingsfor Sentence Representations
Table 3: Time spent per method on all 20 SemEval datasets, 17,608 sentence pairs, and the average time spent on a single sentence pair (time in seconds unless indicated otherwise).
['[EMPTY]', '20 sets', '1 pair']
This considerable difference in numbers of arithmetic operations is also observed in practice. We run tests on a single CPU, using identical code for extracting sentences from the evaluation sets, for every method. The sentence pairs are presented one by one to the models. We disregard the time it takes to load models. Speedups might of course be gained for all methods by presenting the sentences in batches to the models, by computing sentence representations in parallel and by running code on a GPU. However, as we are interested in the differences between the systems, we run the most simple and straightforward scenario. The difference between word2vec and Siamese CBOW is because of a different implementation of word lookup.