Knowledge as A Bridge: Improving Cross-domain Answer Selection withExternal Knowledge
文献类型：会议论文
| 作者 | Yang Deng; Ying Shen; Min Yang; Yaliang Li; Nan Du; Wei Fan; Kai Lei |
| 出版日期 | 2018 |
| 会议日期 | 2018 |
| 会议地点 | New Mexico, USA |
| 英文摘要 | Answer selection is an important but challenging task. Significant progress has been made in domains where a large amount of labeled training data is available. However, obtaining rich annotated data is a time-consuming and expensive process, creating a substantial barrier for applying answer selection models to a new domain which has limited labeled data. In this paper, we propose Knowledge-aware Attentive Network (KAN), a transfer learning framework for crossdomain answer selection, which uses the knowledge base as a bridge to enable knowledge transfer from the source domain to the target domains. Specifically, we design a knowledge module to integrate the knowledge-based representational learning into answer selection models. The learned knowledge-based representations are shared by source and target domains, which not only leverages large amounts of cross-domain data, but also benefits from a regularization effect that leads to more general representations to help tasks in new domains. To verify the effectiveness of our model, we use SQuAD-T dataset as the source domain and three other datasets (i.e., Yahoo QA, TREC QA and InsuranceQA) as the target domains. The experimental results demonstrate that KAN has remarkable applicability and generality, and consistently outperforms the strong competitors by a noticeable margin for cross-domain answer selection. |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/14099]  |
| 专题 | 深圳先进技术研究院_数字所 |
| 推荐引用方式 GB/T 7714 | Yang Deng,Ying Shen,Min Yang,et al. Knowledge as A Bridge: Improving Cross-domain Answer Selection withExternal Knowledge[C]. 见:. New Mexico, USA. 2018. |
入库方式： OAI收割
来源：深圳先进技术研究院
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