Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Revisiting Semantic Representation and Tree Search for Similar Question
  Retrieval   This paper studies the performances of BERT combined with tree structure in
short sentence ranking task. In retrieval-based question answering system, we
retrieve the most similar question of the query question by ranking all the
questions 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 of
time. So we design a specific tree for searching and combine deep model to
solve this problem. We fine-tune BERT on the training data to get semantic
vector or sentence embeddings on the test data. We use all the sentence
embeddings of test data to build our tree based on k-means and do beam search
at predicting time when given a sentence as query. We do the experiments on the
semantic textual similarity dataset, Quora Question Pairs, and process the
dataset for sentence ranking. Experimental results show that our methods
outperform the strong baseline. Our tree accelerate the predicting speed by
500%-1000% without losing too much ranking accuracy.

18
The categories of this paper are: econ
30
The categories of this paper are: q-alg
4
The categories of this paper are: cond-mat
14
The categories of this paper are: math-ph
13
The categories of this paper are: quant-ph
32
The categories of this paper are: atom-ph
9
The categories of this paper are: nlin
31
The categories of this paper are: ao-sci
24
The categories of this paper are: dg-ga
36
The categories of this paper are: cmp-lg
34
The categories of this paper are: chem-ph
10
The categories of this paper are: q-bio
25
The categories of this paper are: patt-sol
3
The categories of this paper are: physics
27
The categories of this paper are: comp-gas
7
The categories of this paper are: hep-th