A typical knowledge-based question answering (KB-QA) system faces two challenges: one is to transform natural language questions into their meaning representations (MRs); the other is to retrieve answers from knowledge bases (KBs) using generated MRs.

Knowledge-based question answering (KB-QA) computes answers to natural language (NL) questions based on existing knowledge bases (KBs).

Unlike existing KB-QA systems which treat semantic parsing and answer retrieval as two cascaded tasks, this paper presents a unified framework that can integrate semantic parsing into the question answering procedure directly.

Our work intersects with two research directions: semantic parsing and question answering .

While it has been shown that paraphrasing methods are useful for question answering (Harabagiu and Hickl, 2006) and relation extraction (Romano et al., 2006), this is, to the best of our knowledge, the first paper to perform semantic parsing through paraphrasing.

We believe that our approach is particularly suitable for scenarios such as factoid question answering , where the space of logical forms is somewhat constrained and a few generation rules suffice to reduce the problem to paraphrasing.

who presented a paraphrase-driven question answering system.

Scaling semantic parsers to large knowledge bases has attracted substantial attention recently (Cai and Yates, 2013; Berant et al., 2013; Kwiatkowski et al., 2013), since it drives applications such as question answering (QA) and information extraction (IE).

Our work relates to recent lines of research in semantic parsing and question answering .

The described state can be modeled with a system of equations whose solution specifies the questions’ answers .

Examples include question answering (Clarke et al., 2010; Cai and Yates, 2013a; Cai and Yates, 2013b; Berant et al., 2013; Kwiatkowski et al.,

We focus on learning from varied supervision, including question answers and equation systems, both can be obtained reliably from annotators with no linguistic training and only basic math knowledge.

Besides SMT, the semantic phrase embeddings can be used in other cross-lingual tasks, such as cross-lingual question answering , since the semantic similarity between phrases in different languages can be calculated accurately.

monolingual NLP tasks which depend on good phrase representations or semantic similarity between phrases, such as named entity recognition, parsing, textual entailment, question answering and paraphrase detection.

cross-lingual question answering) and monolingual applications such as textual entailment, question answering and paraphrase detection.