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In this paper, we approach the problem of semantic search by framing thesearch task as paraphrase span detection, i.e. given a segment of text as aquery phrase, the task is to identify its paraphrase in a given document, thesame modelling setup as typically used in extractive question answering. On theTurku Paraphrase Corpus of 100,000 manually extracted Finnish paraphrase pairsincluding their original document context, we find that our paraphrase spandetection model outperforms two strong retrieval baselines (lexical similarityand BERT sentence embeddings) by 31.9pp and 22.4pp respectively in terms ofexact match, and by 22.3pp and 12.9pp in terms of token-level F-score. Thisdemonstrates a strong advantage of modelling the task in terms of spanretrieval, rather than sentence similarity. Additionally, we introduce a methodfor creating artificial paraphrase data through back-translation, suitable forlanguages where manually annotated paraphrase resources for training the spandetection model are not available.

Semantic Search as Extractive Paraphrase Span Detection