Generating Diverse Descriptions from Semantic Graphs

• URL: http://arxiv.org/abs/2108.05659v2
• Date: Fri, 13 Aug 2021 09:44:02 GMT
• Title: Generating Diverse Descriptions from Semantic Graphs
• Authors: Jiuzhou Han, Daniel Beck, Trevor Cohn
• Abstract summary: We present a graph-to-text model, incorporating a latent variable in an an-decoder model, and its use in an ensemble. We show an ensemble of models produces diverse sets of generated sentences, while retaining similar quality to state-of-the-art models. We evaluate the models on WebNLG datasets in English and Russian, and show an ensemble of models produces diverse sets of generated sentences, while retaining similar quality to state-of-the-art models.
• Score: 38.28044884015192
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Text generation from semantic graphs is traditionally performed with deterministic methods, which generate a unique description given an input graph. However, the generation problem admits a range of acceptable textual outputs, exhibiting lexical, syntactic and semantic variation. To address this disconnect, we present two main contributions. First, we propose a stochastic graph-to-text model, incorporating a latent variable in an encoder-decoder model, and its use in an ensemble. Second, to assess the diversity of the generated sentences, we propose a new automatic evaluation metric which jointly evaluates output diversity and quality in a multi-reference setting. We evaluate the models on WebNLG datasets in English and Russian, and show an ensemble of stochastic models produces diverse sets of generated sentences, while retaining similar quality to state-of-the-art models.

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