Several applications, such as text-to-SQL and computational fact checking, exploit the relationship between relational data and natural language text. However, state of the art solutions simply fail in managing “data-ambiguity", i.e., the case when there are multiple interpretations of the relationship between text and data. Given the ambiguity in language, text can be mapped to different subsets of data, but existing training corpora only have examples in which every sentence/question is annotated precisely w.r.t. the relation. This unrealistic assumption leaves the target applications unable to handle ambiguous cases. To tackle this problem, we present a deep learning method that identifies every pair of data ambiguous attributes and a label that describes both columns. Such metadata can then be used to generate examples with data ambiguities for any input table.

Attribute Ambiguity Discovery: A Deep Learning Approach via Unsupervised Learning

Veltri E.
;
2023-01-01

Abstract

Several applications, such as text-to-SQL and computational fact checking, exploit the relationship between relational data and natural language text. However, state of the art solutions simply fail in managing “data-ambiguity", i.e., the case when there are multiple interpretations of the relationship between text and data. Given the ambiguity in language, text can be mapped to different subsets of data, but existing training corpora only have examples in which every sentence/question is annotated precisely w.r.t. the relation. This unrealistic assumption leaves the target applications unable to handle ambiguous cases. To tackle this problem, we present a deep learning method that identifies every pair of data ambiguous attributes and a label that describes both columns. Such metadata can then be used to generate examples with data ambiguities for any input table.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11563/172895
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