Efficient comparison of sentence embeddings
This work addresses efficiency issues in NLP tasks like semantic similarity for researchers and practitioners, but it is incremental as it compares existing tools without introducing new methods.
The paper tackled the challenge of efficiently comparing sentence embeddings by evaluating FAISS and Elasticsearch on BERT embeddings, finding that FAISS outperforms Elasticsearch in centralized, single-node environments, particularly with large datasets.
The domain of natural language processing (NLP), which has greatly evolved over the last years, has highly benefited from the recent developments in word and sentence embeddings. Such embeddings enable the transformation of complex NLP tasks, like semantic similarity or Question and Answering (Q&A), into much simpler to perform vector comparisons. However, such a problem transformation raises new challenges like the efficient comparison of embeddings and their manipulation. In this work, we will discuss about various word and sentence embeddings algorithms, we will select a sentence embedding algorithm, BERT, as our algorithm of choice and we will evaluate the performance of two vector comparison approaches, FAISS and Elasticsearch, in the specific problem of sentence embeddings. According to the results, FAISS outperforms Elasticsearch when used in a centralized environment with only one node, especially when big datasets are included.