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This paper studies the performances of BERT combined with tree structure inshort sentence ranking task. In retrieval-based question answering system, weretrieve the most similar question of the query question by ranking all thequestions in datasets. If we want to rank all the sentences by neural rankers,we need to score all the sentence pairs. However it consumes large amount oftime. So we design a specific tree for searching and combine deep model tosolve this problem. We fine-tune BERT on the training data to get semanticvector or sentence embeddings on the test data. We use all the sentenceembeddings of test data to build our tree based on k-means and do beam searchat predicting time when given a sentence as query. We do the experiments on thesemantic textual similarity dataset, Quora Question Pairs, and process thedataset for sentence ranking. Experimental results show that our methodsoutperform the strong baseline. Our tree accelerate the predicting speed by500%-1000% without losing too much ranking accuracy.

Revisiting Semantic Representation and Tree Search for Similar Question Retrieval