Jun-Wei Bao

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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. Unlike previous methods which treat them in a cascaded manner , we present a translation-based approach to(More)
A knowledge-based question-answering system (KB-QA) is one that answers natural language questions with information stored in a large-scale knowledge base (<i>KB</i>). Existing KB-QA systems are either powered by curated <i>KBs</i> in which factual knowledge is encoded in entities and relations with well-structured schemas, or by open <i>KBs</i>, which(More)
Most current chatbot engines are designed to reply to user utterances based on existing utterance-response (or Q-R) 1 pairs. In this paper, we present DocChat, a novel information retrieval approach for chat-bot engines that can leverage unstructured documents, instead of Q-R pairs, to respond to utterances. A learning to rank model with features designed(More)
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