Learning to Select, Track, and Generate for Data-to-Text ∗ Hayate Isoy Yui Ueharaz Tatsuya Ishigaki\z Hiroshi Nojiz Eiji Aramakiyz Ichiro Kobayashi[z Yusuke Miyao]z Naoaki Okazaki\z Hiroya Takamura\z yNara Institute of Science and Technology zArtificial Intelligence Research Center, AIST \Tokyo Institute of Technology [Ochanomizu University ]The University of Tokyo fiso.hayate.id3,
[email protected] [email protected] fyui.uehara,ishigaki.t,hiroshi.noji,
[email protected] [email protected] [email protected] Abstract In addition, the salient part moves as the sum- mary explains the data. For example, when gen- We propose a data-to-text generation model erating a summary of a basketball game (Table1 with two modules, one for tracking and the (b)) from the box score (Table1 (a)), the input other for text generation. Our tracking mod- ule selects and keeps track of salient infor- contains numerous data records about the game: mation and memorizes which record has been e.g., Jordan Clarkson scored 18 points. Existing mentioned. Our generation module generates models often refer to the same data record mul- a summary conditioned on the state of track- tiple times (Puduppully et al., 2019). The mod- ing module. Our model is considered to simu- els may mention an incorrect data record, e.g., late the human-like writing process that gradu- Kawhi Leonard added 19 points: the summary ally selects the information by determining the should mention LaMarcus Aldridge, who scored intermediate variables while writing the sum- mary. In addition, we also explore the ef- 19 points.