Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Multi-Task Approach for Disentangling Syntax and Semantics in Sentence
  Representations   We propose a generative model for a sentence that uses two latent variables,
with one intended to represent the syntax of the sentence and the other to
represent its semantics. We show we can achieve better disentanglement between
semantic and syntactic representations by training with multiple losses,
including losses that exploit aligned paraphrastic sentences and word-order
information. We also investigate the effect of moving from bag-of-words to
recurrent neural network modules. We evaluate our models as well as several
popular pretrained embeddings on standard semantic similarity tasks and novel
syntactic similarity tasks. Empirically, we find that the model with the best
performing syntactic and semantic representations also gives rise to the most
disentangled representations.

25
The categories of this paper are: patt-sol
2
The categories of this paper are: math
19
The categories of this paper are: eess
16
The categories of this paper are: stat
30
The categories of this paper are: q-alg
3
The categories of this paper are: physics
36
The categories of this paper are: cmp-lg
24
The categories of this paper are: dg-ga
13
The categories of this paper are: quant-ph
14
The categories of this paper are: math-ph
32
The categories of this paper are: atom-ph
8
The categories of this paper are: hep-ex
15
The categories of this paper are: nucl-ex
10
The categories of this paper are: q-bio
29
The categories of this paper are: funct-an
21
The categories of this paper are: adap-org