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arxiv: 1804.03824 · v4 · pith:XAAAQFB6new · submitted 2018-04-11 · 💻 cs.CL · cs.AI

Reference-less Measure of Faithfulness for Grammatical Error Correction

classification 💻 cs.CL cs.AI
keywords semanticusimcorrectionsourcecorrectionserrorfaithfulnessgrammatical
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We propose USim, a semantic measure for Grammatical Error Correction (GEC) that measures the semantic faithfulness of the output to the source, thereby complementing existing reference-less measures (RLMs) for measuring the output's grammaticality. USim operates by comparing the semantic symbolic structure of the source and the correction, without relying on manually-curated references. Our experiments establish the validity of USim, by showing that (1) semantic annotation can be consistently applied to ungrammatical text; (2) valid corrections obtain a high USim similarity score to the source; and (3) invalid corrections obtain a lower score.

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