  Self-attention networks (SANs) with selective mechanism has produced
substantial improvements in various NLP tasks by concentrating on a subset of
input words. However, the underlying reasons for their strong performance have
not been well explained. In this paper, we bridge the gap by assessing the
strengths of selective SANs (SSANs), which are implemented with a flexible and
universal Gumbel-Softmax. Experimental results on several representative NLP
tasks, including natural language inference, semantic role labelling, and
machine translation, show that SSANs consistently outperform the standard SANs.
Through well-designed probing experiments, we empirically validate that the
improvement of SSANs can be attributed in part to mitigating two commonly-cited
weaknesses of SANs: word order encoding and structure modeling. Specifically,
the selective mechanism improves SANs by paying more attention to content words
that contribute to the meaning of the sentence. The code and data are released
at https://github.com/xwgeng/SSAN.
