Enhancing Content Planning for Table-to-Text Generation with Data Understanding and Verification (2020)

Abstract: Neural table-to-text models, which select and order salient data, as well as verbalizing them fluently via surface realization, have achieved promising progress. Based on results from previous work, the performance bottleneck of current models lies in the stage of content planing (selecting and ordering salient content from the input). That is, performance drops drastically when an oracle content plan is replaced by a model-inferred one during surface realization. In this paper, we propose to enhance neural content planning by (1) understanding data values with contextual numerical value representations that bring the sense of value comparison into content planning; (2) verifying the importance and ordering of the selected sequence of records with policy gradient. We evaluated our model on ROTOWIRE and MLB, two datasets on this task, and results show that our model outperforms existing systems with respect to content planning metrics.

## introduction table -to-text generation refers to the task of generating text from structured data. models for this task can be mainly categorized into two types: pipeline-style models, which decompose the generation process into sequential stages, including content planning (stage 1, selecting and ordering salient content from the input) and surface realization (stage 2, converting the content plan to surface string) @xcite ; and end-to-end models, which entangle aforementioned stages and generate text directly from structured data through a neural encoder-decoder framework @xcite . as in fig. 1 , this task provides tables with redundant records. each record has three elements: table row header (entity, e.g. conley), table column header (type, e.g. points) and table cell @xcite . models are expected to generate descriptive text ## approach we propose to improve content planning (stage 1 of ncp) from two aspects: (i) during record encoding, we design a contextual numeric value representation to improve the understanding of entities' (players' and teams') performance; (ii) a reinforced training strategy with targeted supervision signals is used to compensate maximizing the mle in pointer network to boost model's content planning ability. fig. 2 illustrates the overall training procedure. we first pre-train a model to learn contextual numeric value representations to understand relationship between records' numeric values by pairwise ranking loss. secondly, given the pretrained model and table s , we encode each record with its contextual numeric value representation. in decoding phase of stage 1, the pointer network is guided to favor important records for content planning with the help of reinforced supervision signals. stage 2 remains the same as in the base model. we describe details in following parts. ## conclusion in order to enhance neural content planning for table-to-text generation, we proposed (1) contextual numerical value representations to help model understand data values and (2) effective rewards to verify a model's inferred important records during training. experimental results show that our model outperforms competitive baselines in terms of content planning. in the future, we would like to explore enhancement on surface realization jointly to generate better text.