Learning to Select, Track, and Generate for Data-to-Text

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2102–2113 Florence, Italy, July 28 - August 2, 2019. c⃝2019 Association for Computational Linguistics 2102 Learning to Select, Track, and Generate for Data-to-Text Hayate Iso ∗† Yui Uehara‡ Tatsuya Ishigaki♮‡ Hiroshi Noji‡ Eiji Aramaki†‡ Ichiro Kobayashi♭‡ Yusuke Miyao♯‡ Naoaki Okazaki♮‡ Hiroya Takamura♮‡ †Nara Institute of Science and Technology ‡Artificial Intelligence Research Center, AIST ♮Tokyo Institute of Technology ♭Ochanomizu University ♯The University of Tokyo {iso.hayate.id3,aramaki}@is.naist.jp koba@is.ocha.ac.jp {yui.uehara,ishigaki.t,hiroshi.noji,takamura.hiroya}@aist.go.jp yusuke@is.s.u-tokyo.ac.jp okazaki@c.titech.ac.jp Abstract We propose a data-to-text generation model with two modules, one for tracking and the other for text generation. Our tracking mod- ule selects and keeps track of salient infor- mation and memorizes which record has been mentioned. Our generation module generates a summary conditioned on the state of track- ing module. Our model is considered to simu- late the human-like writing process that gradu- ally selects the information by determining the intermediate variables while writing the sum- mary. In addition, we also explore the ef- fectiveness of the writer information for gen- eration. Experimental results show that our model outperforms existing models in all eval- uation metrics even without writer informa- tion. Incorporating writer information fur- ther improves the performance, contributing to content planning and surface realization. 1 Introduction Advances in sensor and data storage technolo- gies have rapidly increased the amount of data produced in various fields such as weather, fi- nance, and sports. In order to address the infor- mation overload caused by the massive data, data- to-text generation technology, which expresses the contents of data in natural language, becomes more important (Barzilay and Lapata, 2005). Re- cently, neural methods can generate high-quality short summaries especially from small pieces of data (Liu et al., 2018). Despite this success, it remains challenging to generate a high-quality long summary from data (Wiseman et al., 2017). One reason for the difficulty is because the input data is too large for a naive model to find its salient part, i.e., to deter- mine which part of the data should be mentioned. ∗Work was done during the internship at Artificial Intel- ligence Research Center, AIST In addition, the salient part moves as the sum- mary explains the data. For example, when gen- erating a summary of a basketball game (Table 1 (b)) from the box score (Table 1 (a)), the input contains numerous data records about the game: e.g., Jordan Clarkson scored 18 points. Existing models often refer to the same data record mul- tiple times (Puduppully et al., 2019). The mod- els may mention an incorrect data record, e.g., Kawhi Leonard added 19 points: the summary should mention LaMarcus Aldridge, who scored 19 points. Thus, we need a model that finds salient parts, tracks transitions of salient parts, and ex- presses information faithful to the input. In this paper, we propose a novel data-to- text generation model with two modules, one for saliency tracking and another for text generation. The tracking module keeps track of saliency in the input data: …

Content truncated to 500 words