基于行、列、时间三维分层编码器的表格文本生成
Table-to-Text Generation with Effective Hierarchical Encoder on Three Dimensions (Row, Column and Time)
摘要：
虽然用于表到文本生成的Seq2Seq模型已经取得了显著的进展，但是在一维中对表表示进行建模是不够的。这是因为（1）表由多个行和列组成，这意味着对表进行编码不应仅依赖于一维序列或记录集，以及（2）大多数表是时间序列数据（例如NBA比赛数据、股票市场数据），这意味着当前表的描述可能受到其历史数据的影响。为了解决上述问题，我们不仅考虑同一行中的其他记录来对每个表格单元格进行建模，还通过在同一列中的其他单元格的上下文中或分别使用历史（时间维度）数据来对每个表格单元格进行建模来丰富表格的表示。此外，我们开发了一个表格单元格融合门，根据每个维度的表示的显著性，将行，列和时间维度的表示组合成一个稠密向量。我们在ROTOWIRE上评估了我们的方法，ROTOWIRE是NBA篮球比赛的基准数据集。自动和人工评估结果都证明了我们的模型的有效性，在BLEU中比最先进的模型提高了2.66。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
