Although Seq2Seq models for table-to-text generation have achieved remarkable
progress, modeling table representation in one dimension is inadequate. This is
because (1) the table consists of multiple rows and columns, which means that
encoding a table should not depend only on one dimensional sequence or set of
records and (2) most of the tables are time series data (e.g. NBA game data,
stock market data), which means that the description of the current table may
be affected by its historical data. To address aforementioned problems, not
only do we model each table cell considering other records in the same row, we
also enrich table's representation by modeling each table cell in context of
other cells in the same column or with historical (time dimension) data
respectively. In addition, we develop a table cell fusion gate to combine
representations from row, column and time dimension into one dense vector
according to the saliency of each dimension's representation. We evaluated our
methods on ROTOWIRE, a benchmark dataset of NBA basketball games. Both
automatic and human evaluation results demonstrate the effectiveness of our
model with improvement of 2.66 in BLEU over the strong baseline and
outperformance of state-of-the-art model.

本文主要介绍了一个改进型的 Seq2Seq 模型，可用于将多维表格转化为文本，并针对表的多个维度建模，并在 NBA 篮球比赛数据的基准数据集 ROTOWIRE 上进行了评估，效果明显。