Selecting, Planning, and Rewriting: A Modular Approach for Data-to-Document Generation and Translation

By Lesly Miculicich, Marc Marone, Hany Hassan. 2019

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In this paper, we report our system submissions to all 6 tracks of the WNGT 2019 shared task on Document-Level Generation and Translation. The objective is to generate a textual document from either structured data: generation task, or a document in a different language: translation task. For the translation task, we focused on adapting a large scale system trained on WMT data by fine tuning it on the RotoWire data. For the generation task, we participated with two systems based on a selection and planning model followed by (a) a simple language model generation, and (b) a GPT-2 pre-trained language model approach. The selection and planning module chooses a subset of table records in order, and the language models produce text given such a subset.

Subfields: Artificial Intelligence, Molecular Biology, Natural Language Processing

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Automatic Detection of Generated Text is Easiest when Humans are Fooled (2019, background): “5 (Miculicich et al., 2019)), the distributional deviations that plague top-k text will surface in nucleus sampling as well.”