S$^3$HQA: A Three-Stage Approach for Multi-hop Text-Table Hybrid
  Question Answering
Fangyu Lei Xiang Li Yifan Wei Shizhu He Yiming Huang Jun Zhao Kang Liu
Answering multi-hop questions over hybrid factual knowledge from the given
text and table (TextTableQA) is a challenging task. Existing models mainly
adopt a retriever-reader framework, which have several deficiencies, such as
noisy labeling in training retriever, insufficient utilization of heterogeneous
information over text and table, and deficient ability for different reasoning
operations. In this paper, we propose a three-stage TextTableQA framework
S3HQA, which comprises of retriever, selector, and reasoner. We use a retriever
with refinement training to solve the noisy labeling problem. Then, a hybrid
selector considers the linked relationships between heterogeneous data to
select the most relevant factual knowledge. For the final stage, instead of
adapting a reading comprehension module like in previous methods, we employ a
generation-based reasoner to obtain answers. This includes two approaches: a
row-wise generator and an LLM prompting generator~(first time used in this
task). The experimental results demonstrate that our method achieves
competitive results in the few-shot setting. When trained on the full dataset,
our approach outperforms all baseline methods, ranking first on the HybridQA
leaderboard.