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Diversity Enhanced Table-to-Text Generation via Type Control

2022-05-22 22:05:21
Yotam Perlitz, Liat Ein-Dot, Dafna Sheinwald, Noam Slonim, Michal Shmueli-Scheuer
arXiv_CL
     
Abstract

Generating natural language statements to convey information from tabular data (i.e., Table-to-text) is a process with one input and a variety of valid outputs. This characteristic underscores the abilities to control the generation and produce a diverse set of outputs as two key assets. Thus, we propose a diversity enhancing scheme that builds upon an inherent property of the statements, namely, their logic-types, by using a type-controlled Table-to-text generation model. Employing automatic and manual tests, we prove its twofold advantage: users can effectively tune the generated statement type, and, by sampling different types, can obtain a diverse set of statements for a given table.

Abstract (translated)

URL

https://arxiv.org/abs/2205.10938

PDF

https://arxiv.org/pdf/2205.10938.pdf

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