Data-to-text Generation with Macro Planning

Data-to-text Generation with Macro Planning Ratish Puduppully and Mirella Lapata Institute for Language, Cognition and Computation School of Informatics, University of Edinburgh 10 Crichton Street, Edinburgh EH8 9AB r.puduppully@sms.ed.ac.uk mlap@inf.ed.ac.uk 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 select- ing appropriate content and ordering it co- herently. 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 em- brace separate modules for planning and sur- face 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 compet- itive baselines in terms of automatic and hu- man evaluation. 1 Introduction Data-to-text generation refers to the task of gen- erating textual output from non-linguistic input (Reiter and Dale, 1997, 2000; Gatt and Krahmer, 2018) such as databases of records, simulations of physical systems, accounting spreadsheets, or expert system knowledge bases. As an example, Figure 1 shows various statistics describing a major league baseball (MLB) game, including extracts from the box score (i.e., the perfor- mance of the two teams and individual team members who played as batters, pitchers or field- ers; Table (A)), play-by-play (i.e., the detailed sequence of each play of the game as it occurred; Table (B)), and a human written game summary (Table (C)). Traditional methods for data-to-text generation (Kukich, 1983; McKeown, 1992; Reiter and Dale, 1997) follow a pipeline architecture, adopting separate stages for text planning (determining which content to talk about and how it might be organized in discourse), sentence planning (aggregating content into sentences, deciding spe- cific words to describe concepts and relations, and generating referring expressions), and linguistic realization (applying the rules of syntax, mor- phology, and orthographic processing to generate surface forms). Recent neural network–based approaches (Lebret et al., 2016; Mei et al., 2016; Wiseman et al., 2017) make use of the encoder- decoder architecture (Sutskever et al., 2014), are trained end-to-end, and have no special-purpose modules for how to best generate a text, aside from generic mechanisms such as attention and copy (Bahdanau et al., 2015; Gu et al., 2016). The popularity of end-to-end models has been fur- ther boosted by the release of new datasets with thousands of input-document training pairs. The example shown in Figure 1 is taken from the MLB dataset (Puduppully et al., 2019b), which contains baseball game statistics and human written sum- maries (∼25K instances). ROTOWIRE (Wiseman et al., 2017) is another widely used benchmark, which contains NBA basketball game statistics and their descriptions (∼5K instances). Wiseman et al. (2017) show that despite being able to generate fluent text, neural data-to-text generation models are often imprecise, prone to hallucination (i.e., generate text that is not …

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