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Incremental dialogue model components produce a sequence of output prefixesbased on incoming input. Mistakes can occur due to local ambiguities or towrong hypotheses, making the ability to revise past outputs a desirableproperty that can be governed by a policy. In this work, we formalise andcharacterise edits and revisions in incremental sequence labelling and proposemetrics to evaluate revision policies. We then apply our methodology to profilethe incremental behaviour of three Transformer-based encoders in various tasks,paving the road for better revision policies.

The Road to Quality is Paved with Good Revisions: A Detailed Evaluation Methodology for Revision Policies in Incremental Sequence Labelling