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Semantic Equivalence Detection: Are Interrogatives Harder than Declaratives?

Duplicate Question Detection (DQD) is a Natural Language Processing task under active research, with applications to fields like Community Question Answering and Information Retrieval. While DQD falls under the umbrella of Semantic Text Similarity (STS), these are often not seen as similar tasks of semantic equivalence detection, with STS being implicitly understood as concerning only declarative sentences. Nevertheless, approaches to STS have been applied to DQD and paraphrase detection, that is to interrogatives and declaratives, alike. We present a study that seeks to assess, under conditions of comparability, the possible different performance of state-of-the-art approaches to STS over different types of textual segments, including most notably declaratives and interrogatives. This paper contributes to a better understanding of current mainstream methods for semantic equivalence detection, and to a better appreciation of the different results reported in the literature when these are obtained from different data sets with different types of textual segments. Importantly, it contributes also with results concerning how data sets containing textual segments of a certain type can be used to leverage the performance of resolvers for segments of other types.