Table-to-Text: Describing Table Region with Natural Language
This addresses the challenge of making tabular data more accessible through natural language, though it is incremental with specific gains in accuracy.
The paper tackles the problem of generating natural language descriptions for table regions, such as rows, by introducing a generative model with a copying mechanism for rare words. It improves state-of-the-art BLEU-4 scores on synthetic datasets and outperforms baselines on a new open-domain dataset.
In this paper, we present a generative model to generate a natural language sentence describing a table region, e.g., a row. The model maps a row from a table to a continuous vector and then generates a natural language sentence by leveraging the semantics of a table. To deal with rare words appearing in a table, we develop a flexible copying mechanism that selectively replicates contents from the table in the output sequence. Extensive experiments demonstrate the accuracy of the model and the power of the copying mechanism. On two synthetic datasets, WIKIBIO and SIMPLEQUESTIONS, our model improves the current state-of-the-art BLEU-4 score from 34.70 to 40.26 and from 33.32 to 39.12, respectively. Furthermore, we introduce an open-domain dataset WIKITABLETEXT including 13,318 explanatory sentences for 4,962 tables. Our model achieves a BLEU-4 score of 38.23, which outperforms template based and language model based approaches.