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

Heng Gong, Xiaocheng Feng, Bing Qin, Ting Liu


Abstract
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.
Anthology ID:
D19-1310
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
3143–3152
Language:
URL:
https://aclanthology.org/D19-1310
DOI:
10.18653/v1/D19-1310
Bibkey:
Cite (ACL):
Heng Gong, Xiaocheng Feng, Bing Qin, and Ting Liu. 2019. Table-to-Text Generation with Effective Hierarchical Encoder on Three Dimensions (Row, Column and Time). In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 3143–3152, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Table-to-Text Generation with Effective Hierarchical Encoder on Three Dimensions (Row, Column and Time) (Gong et al., EMNLP 2019)
Copy Citation:
PDF:
https://aclanthology.org/D19-1310.pdf
Code
 ernestgong/data2text-three-dimensions
Data
RotoWire