Efficient One-Pass End-to-End Entity Linking for Questions

被引:0
作者
Li, Belinda Z. [1 ]
Min, Sewon [2 ]
Iyer, Srinivasan [3 ]
Mehdad, Yashar [3 ]
Yih, Wen-Tau [3 ]
机构
[1] MIT, CSAIL, Cambridge, MA 02139 USA
[2] Washington Univ, St Louis, MO 14263 USA
[3] Facebook AI, Menlo Pk, CA USA
来源
PROCEEDINGS OF THE 2020 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING (EMNLP) | 2020年
关键词
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
We present ELQ, a fast end-to-end entity linking model for questions, which uses a biencoder to jointly perform mention detection and linking in one pass. Evaluated on WebQSP and GraphQuestions with extended annotations that cover multiple entities per question, ELQ outperforms the previous state of the art by a large margin of +12.7% and +19.6% F1, respectively. With a very fast inference time (1.57 examples/s on a single CPU), ELQ can be useful for downstream question answering systems. In a proof-of-concept experiment, we demonstrate that using ELQ significantly improves the downstream QA performance of GraphRetriever (Min et al., 2019).(1)
引用
收藏
页码:6433 / 6441
页数:9
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