Explainable Multi-Hop Question Answering for QA Assistants: Two-Hop Evidence Retrieval, Sentence-Level Supporting Facts, and Explicit Reasoning Paths - Xiaofei Luo (University of Science and Computer Technology, 2026)
Multi-hop question answering (QA) for customer-facing assistants requires not only accurate answers but also an auditable evidence trail that explains how the system arrived at each answer. We present a fully interpretable multi-hop QA pipeline that decomposes inference into three explicit modules—Retriever → Evidence Selector → Reasoner—and produces an explanation consisting of sentence-level supporting facts and an explicit two-hop evidence path. The retriever ranks candidate paragraphs using lexical IDF-weighted token overlap; the evidence selector chooses a small set of high-scoring sentences; and the reasoner extracts a final answer using weighted candidate phrase scoring and deterministic rules for date/number and constrained yes/no comparisons. We conduct full experimental evaluations on the complete development splits of HotpotQA (7,405 questions, distractor setting) and 2WikiMultihopQA (12,576 questions). On HotpotQA, sentence-level evidence selection improves Supporting Fact F1 from 0.334 to 0.419, and adding an explicit two-hop retrieval path further increases Supporting Fact F1 to 0.426 while raising paragraph recall@2 to 0.603. Answer F1 increases from 0.084 to 0.088. On 2WikiMultihopQA, evidence selection improves Supporting Fact F1 from 0.328 to 0.429 and Answer F1 from 0.071 to 0.075. These results quantify the contribution of explicit evidence selection and path-constrained retrieval to explainability and provide a practical, reproducible baseline for knowledge assistants that must justify answers with supporting facts.