Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Inducing Constituency Trees through Neural Machine Translation   Latent tree learning(LTL) methods learn to parse sentences using only
indirect supervision from a downstream task. Recent advances in latent tree
learning have made it possible to recover moderately high quality tree
structures by training with language modeling or auto-encoding objectives. In
this work, we explore the hypothesis that decoding in machine translation, as a
conditional language modeling task, will produce better tree structures since
it offers a similar training signal as language modeling, but with more
semantic signal. We adapt two existing latent-tree language models--PRPN
andON-LSTM--for use in translation. We find that they indeed recover trees that
are better in F1 score than those seen in language modeling on WSJ test set,
while maintaining strong translation quality. We observe that translation is a
better objective than language modeling for inducing trees, marking the first
success at latent tree learning using a machine translation objective.
Additionally, our findings suggest that, although translation provides better
signal for inducing trees than language modeling, translation models can
perform well without exploiting the latent tree structure.

31
The categories of this paper are: ao-sci
22
The categories of this paper are: chao-dyn
6
The categories of this paper are: astro-ph
34
The categories of this paper are: chem-ph
12
The categories of this paper are: nucl-th
20
The categories of this paper are: acc-phys
8
The categories of this paper are: hep-ex
4
The categories of this paper are: cond-mat
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The categories of this paper are: dg-ga
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The categories of this paper are: solv-int
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The categories of this paper are: hep-lat
5
The categories of this paper are: gr-qc
21
The categories of this paper are: adap-org
0
The categories of this paper are: hep-ph
2
The categories of this paper are: math
30
The categories of this paper are: q-alg