Crowd-sourcing NLG Data: Pictures Elicit Better Data

1 Aug 2016Jekaterina NovikovaOliver LemonVerena Rieser

Recent advances in corpus-based Natural Language Generation (NLG) hold the promise of being easily portable across domains, but require costly training data, consisting of meaning representations (MRs) paired with Natural Language (NL) utterances. In this work, we propose a novel framework for crowdsourcing high quality NLG training data, using automatic quality control measures and evaluating different MRs with which to elicit data... (read more)

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