Jiawei Zhou, Xiaoguang Li, Lifeng Shang, Lan Luo, Ke Zhan, Enrui Hu, Xinyu Zhang, Hao Jiang, Zhao Cao, Fan Yu, Xin Jiang, Qun Liu, Lei Chen. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2022.

Nowadays, multi-hop question answer (QA) problem is challenging and not well solved in the QA community. The dominant bottleneck of the multi-hop QA problem is the need for a reasoning retriever to fetch a document path from an open-domain corpus (e.g., Wikipedia). A reasoning retriever aims to collect an evidence document from large corpora at one hop retrieval and aggregate the evidence for subsequent hop retrieval, which yields a document path after multi-hop retrieval. There exist two challenges, (1) to fetch the evidence document in an efficient and explainable way at one hop retrieval and (2) to update the question information by aggregating the evidence from the retrieved document after each hop retrieval. To address these two challenges, we propose a triple-fact-based retrieval model to effectively retrieve a related document path in an explainable way for each question. We extract a structured representation from the unstructured document and utilize the knowledge of pre-trained language model (PLM) to do the semantic-level matching between the question and document. We evaluate the proposed Triple-fact Retriever model on the recently proposed open-domain multi-hop QA dataset, HotpotQA, and a cross-document multi-step Reading Comprehension dataset, Wikihop. The results11The source code is available on our website: https://github.com/Rebaccamin/triple_retriever. demonstrate that the Triple-fact retriever outperforms the existing baseline retrieval works.

Currently, the most popular method for open-domain Question Answering (QA) adopts "Retriever and Reader" pipeline, where the retriever extracts a list of candidate documents from a large set of documents followed by a ranker to rank the most relevant documents and the reader extracts answer from the candidates. Existing studies take the greedy strategy in the sense that they only use samples for ranking at the current hop, and ignore the global information across the whole documents. In this paper, we propose a purely rank-based framework Thinking Path Re-Ranker (TPRR), which is comprised of Thinking Path Ranker (TPR) for generating document sequences called "a path" and External Path Reranker (EPR) for selecting the best path from candidate paths generated by TPR. Specifically, TPR leverages the scores of a dense model and conditional probabilities to score the full paths. Moreover, to further enhance the performance of the dense ranker in the iterative training, we propose a "thinking" negatives selection method that the top-K candidates treated as negatives in the current hop are adjusted dynamically through supervised signals. After achieving multiple supporting paths through TPR, the EPR component which integrates several fine-grained training tasks for QA is used to select the best path for answer extraction. We have tested our proposed solution on the multi-hop dataset "HotpotQA" with a full wiki set ting, and the results show that TPRR significantly outperforms the existing state-of-the-art models. Moreover, our method has won the first place in the HotpotQA official leaderboard since Feb 1, 2021 under the Fullwiki setting. Code is available at https://gitee.com/mindspore/mindspore/ tree/master/model_zoo/research/nlp/tprr.