Table-to-Text Generation with Effective Hierarchical Encoder on Three Dimensions (Row, Column and Time)

IJCNLP 2019 Heng GongXiaocheng FengBing QinTing Liu

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... (read more)

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