Effective Search of Logical Forms for Weakly Supervised Knowledge-Based Question Answering
- Publication Type:
- Conference Proceeding
- Citation:
- IJCAI International Joint Conference on Artificial Intelligence, 2020, 2021-January, pp. 2227-2233
- Issue Date:
- 2020-03-05
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Many algorithms for Knowledge-Based Question Answering (KBQA) depend on
semantic parsing, which translates a question to its logical form. When only
weak supervision is provided, it is usually necessary to search valid logical
forms for model training. However, a complex question typically involves a huge
search space, which creates two main problems: 1) the solutions limited by
computation time and memory usually reduce the success rate of the search, and
2) spurious logical forms in the search results degrade the quality of training
data. These two problems lead to a poorly-trained semantic parsing model. In
this work, we propose an effective search method for weakly supervised KBQA
based on operator prediction for questions. With search space constrained by
predicted operators, sufficient search paths can be explored, more valid
logical forms can be derived, and operators possibly causing spurious logical
forms can be avoided. As a result, a larger proportion of questions in a weakly
supervised training set are equipped with logical forms, and fewer spurious
logical forms are generated. Such high-quality training data directly
contributes to a better semantic parsing model. Experimental results on one of
the largest KBQA datasets (i.e., CSQA) verify the effectiveness of our
approach: improving the precision from 67% to 72% and the recall from 67% to
72% in terms of the overall score.
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