Few-shot Multi-hop Question Answering over Knowledge Base
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Previous work on Chinese Knowledge Base Question Answering has been restricted due to the lack of complex Chinese semantic parsing dataset and the exponentially growth of searching space with the length of relation paths. This paper proposes an efficient pipeline method equipped with a pre-trained language model and a strategy to construct artificial training samples, which only needs small amount of data but performs well on open-domain complex Chinese Question Answering task. Besides, By adopting a Beam Search algorithm based on a language model marking scores for candidate query tuples, we decelerate the growing relation paths when generating multi-hop query paths. Finally, we evaluate our model on CCKS2019 Complex Question Answering via Knowledge Base task and achieves F1-score of 62.55% on the test dataset. Moreover when training with only 10% data, our model can still achieves F1-score of 58.54%. The result shows the capability of our model to process KBQA task and the advantage in few-shot learning.
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