SLiQA-I: Towards Cold-start Development of End-to-end Spoken Language Interface for Question Answering

By Yilin Shen, Yu Wang, Abhishek Patel, Hongxia Jin. 2019

8 citations recorded by the source corpus.

Question answering (QA) has become a key capability for voice enabled personal assistants to automatically answer various user questions. However, the development of a spoken language interface for QA in a new domain is time consuming and requires a lot of human labors. Thus, it is crucially desirable to design an end-to-end system, referred to as SliQA, that can facilitate developers to easily and quickly build a QA interface from scratch and output a high quality plug-and-play QA engine. In this paper, we take the first step of SliQA system design, named SliQA-I, to support answering factoid questions regarding an entity over existing knowledge graphs. SliQA-I incorporates a novel iterative human-in-the-loop question generator and an enhanced deep coupled QA engine, thereby requiring light human workload. We implement the real system and evaluate it on three domains from different aspects. The results show that the QA performance of SliQA-I achieves up to 3.58% accuracy gain compared with baseline approaches which use existing QA engine on human generated data. More importantly, we show that SliQA-I only takes as low as 0.025 second to generate a question which has similar quality as human generated ones in terms of both naturalness and grammatical correctness.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

  1. Xiangyu Zeng
  2. Hongxia Jin
  3. Yilin Shen
  4. Yixuan Li
  5. Zixuan Ke

In Computer Vision and Pattern Recognition

  1. Hongxia Jin
  2. Yilin Shen
  3. Yixuan Li
  4. Zixuan Ke

Citations in the corpus