  Previous works have demonstrated the effectiveness of utilising pre-trained
sentence encoders based on their sentence representations for meaning
comparison tasks. Though such representations are shown to capture hidden
syntax structures, the direct similarity comparison between them exhibits weak
sensitivity to word order and structural differences in given sentences. A
single similarity score further makes the comparison process hard to interpret.
Therefore, we here propose to combine sentence encoders with an alignment
component by representing each sentence as a list of predicate-argument spans
(where their span representations are derived from sentence encoders), and
decomposing the sentence-level meaning comparison into the alignment between
their spans for paraphrase identification tasks. Empirical results show that
the alignment component brings in both improved performance and
interpretability for various sentence encoders. After closer investigation, the
proposed approach indicates increased sensitivity to structural difference and
enhanced ability to distinguish non-paraphrases with high lexical overlap.
