研究内容

• providing NL explanations to query answers（为查询答案提供自然语言解释）
• The answers that we present are importantly based on the provenance of tuples in the query result, detailing not only the results but also their explanations.（我们给出的答案主要基于查询结果中元组的出处，不仅详细说明了结果，还详细说明了它们的解释。）

技术方案

• We propose a novel approach of presenting provenance information for answers of NL queries.The method can transform provenance information to NL, by leveraging the original NL query structure. （我们提出了一种为 NL 查询的答案呈现出处信息的新方法。这个方法利用原始 NL 查询结构将出处信息转换为自然语言）
  1. modify NaLIR so that we store exactly which parts of the NL query translate to which parts of the formal query.（修改NaLIR，以便准确地存储NL查询的哪些部分转换为正式查询的哪些部分）
  2. modify the provenance aware engine so that it stores which parts of the formal query “contribute” to which parts of the provenance.（修改起源感知引擎，以便它存储正式查询的哪些部分“贡献”到出处的哪些部分）
  3. By composing these two “mappings” (text-to-query-parts and query-parts-to-provenance) we infer which parts of the NL query text are related to which provenance parts.（通过组合这两个“映射”(文本到查询部分和查询部分到出处)，我们可以推断出NL查询文本的哪些部分与哪些出处部分相关。）
  4. Finally, we use the latter information in an “inverse” manner, to translate the provenance to NL text.（最后，我们以一种“反向”的方式使用后一种信息，将出处转换为NL文本。）
• since provenance information is typically large and complex, we present two solutions for its effective presentation as NL text:（由于出处信息通常很大且很复杂，因此我们提出了两种解决方案，以将其有效地呈现为 NL 文本：一种基于出处分解，具有与 NL 案例相关的新需求，另一种基于摘要。）
  • one that is based on provenance factorization, with novel desiderata relevant to the NL case.
  • and one that is based on summarization.