Data-to-text Generation with Macro Planning

By Ratish Puduppully, Mirella Lapata. 2021

91 citations recorded by the source corpus.

Abstract Recent approaches to data-to-text generation have adopted the very successful encoder-decoder architecture or variants thereof. These models generate text that is fluent (but often imprecise) and perform quite poorly at selecting appropriate content and ordering it coherently. To overcome some of these issues, we propose a neural model with a macro planning stage followed by a generation stage reminiscent of traditional methods which embrace separate modules for planning and surface realization. Macro plans represent high level organization of important content such as entities, events, and their interactions; they are learned from data and given as input to the generator. Extensive experiments on two data-to-text benchmarks (RotoWire and MLB) show that our approach outperforms competitive baselines in terms of automatic and human evaluation.

Subfields: Artificial Intelligence, Information Systems

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Sebastian Gehrmann
2. Sebastian Ruder
3. Ankur P. Parikh
4. Jan A. Botha
5. Vitaly Nikolaev
6. Clara Rivera
7. Michael Chavinda
8. Graham Neubig

In Information Systems

1. Ankur P. Parikh
2. Sebastian Ruder
3. Sebastian Gehrmann
4. Vitaly Nikolaev

Citations in the corpus

• TaTa: A Multilingual Table-to-Text Dataset for African Languages (2022, background): “…include ones with the goal to generate Wikipedia texts (Lebret et al., 2016; Parikh et al., 2020) and sport commentary (Wiseman et al., 2017; Thomson et al., 2020; Puduppully and Lapata, 2021) T A TA deals with generating text based on information in charts, following the second task setup.”