DecAF: Joint Decoding of Answers and Logical Forms for Question Answering over Knowledge Bases

Question answering over knowledge bases (KBs) aims to answer natural language questions with factual information such as entities and relations in KBs. Previous methods either generate logical forms that can be executed over KBs to obtain final answers or predict answers directly. Empirical results show that the former often produces more accurate answers, but it suffers from non-execution issues due to potential syntactic and semantic errors in the generated logical forms. AF that jointly generates both logical forms and direct answers, and then combines the merits of them to get the final answers. AF is based on simple free-text retrieval without relying on any entity linking tools -- this simplification eases its adaptation to different datasets. AF achieves new stateof-the-art accuracy on WebQSP, FreebaseQA, and GrailQA benchmarks, while getting competitive results on the ComplexWebQuestions benchmark. Knowledge Bases Question Answering (KBQA) aims to answer natural language questions based on knowledge from KBs such as DBpedia (Auer et al., 2007), Freebase (Bollacker et al., 2008) or Wikidata (Vrandečić & Krötzsch, 2014). Existing methods can be divided into two categories. One category is based on semantic parsing, where models first parse the input question into a logical form (e.g., SPARQL (hommeaux, 2011) or S-expression (Gu et al., 2021)) then execute the logical form against knowledge bases to obtain the final answers (Das et al., 2021; Gu et al., 2021; Ye et al., 2022). They either classify the entities in KB to decide which are the answers (Sun et al., 2019) or generate the answers using a sequence-to-sequence framework (Saxena et al., 2022; Oğuz et al., 2022). Previous empirical results (Ye et al., 2022; Das et al., 2021; Gu et al., 2022) show that the semantic parsing based methods can produce more accurate answers over benchmark datasets. However, due to the syntax and semantic restrictions, the output logical forms can often be non-executable and thus would not produce any answers. On the other hand, direct-answer-prediction methods can guarantee to generate output answers, albeit their answer accuracy is usually not as good as semantic parsing based methods, especially over complex questions which require multi-hop reasoning (Talmor & Berant, 2018).