Abstract
Table retrieval aims to rank candidate tables for answering natural language query, in which the most critical problem is how to learn informative representations for structured tables. Most previous methods roughly flatten the table and send it into a sequence encoder, ignoring the structure information of tables and the semantic interaction between table cells and contexts. In this paper, we propose a dual graph based method to perceive the semantics and structure of tables, so as to preferably support the downstream table retrieval task. Inspired by human cognition, we first decouple a table into the row view and column view, then build dual graphs from these two views with the consideration of table contexts. Afterward, intra-graph and inter-graph interactions are iteratively performed for aggregating and exchanging local row- and column-oriented features respectively, and an adaptive fusion strategy is eventually tailor-made for sophisticated table representations. In this way, the table structure and semantic information are well considered with dual-graph modeling. Consequently, the input query can match the target tables based on their full-fledged table representations and achieve the ultimate ranking results more accurately. Extensive experiments verify the superiority of our dual graphs over strong baselines on two table retrieval datasets WikiTables and WebQueryTable. Further analyses also confirm the adaptability for row-/column-oriented tables, and show the rationality and generalization of dual graphs. The source code is available at https://github.com/ty33123/DualG.
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Acknowledgment
This work is supported by the National Key Research and Development Program of China (grant No.2021YFB3100600), the Strategic Priority Research Program of Chinese Academy of Sciences (grant No.XDC02040400) and the Youth Innovation Promotion Association of CAS (Grant No. 2021153).
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I understand that using technology can have ethical implications, especially in collection, processing, and privacy of form retrieval data. I acknowledge and recognize the importance of complying with ethical standards and the hazards of potential risks.
In the data collection and processing, my training data comes from two publicly available tabular search datasets. Although we do not collect or store any sensitive information, we should strictly restrict the retrieval text of users and ensure that it does not contain any dangerous information.
In addition, when the model used in police or military related applications, we should pay special attention to its use in these areas, which must conducted in a more responsible manner. To prevent models from providing inaccurate search results for police or military personnel, users are responsible for ensuring that they comply with ethical principles and laws and regulations when using model outputs, and for screening search results.
In summary, I strive to ensure that the model outputs search results in an ethical and responsible manner, and I urge my users to do the same. I will continue to adhere to ethical standards and stay abreast of emerging ethical issues in the fields of machine learning and data mining.
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Liu, T. et al. (2023). Enhancing Table Retrieval with Dual Graph Representations. In: Koutra, D., Plant, C., Gomez Rodriguez, M., Baralis, E., Bonchi, F. (eds) Machine Learning and Knowledge Discovery in Databases: Research Track. ECML PKDD 2023. Lecture Notes in Computer Science(), vol 14172. Springer, Cham. https://doi.org/10.1007/978-3-031-43421-1_7
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