arXiv:2010.02413cs.CL
Efficient One-Pass End-to-End Entity Linking for Questions
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Abstract
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 (arXiv:1911.03868). Code and data available at https://github.com/facebookresearch/BLINK/tree/master/elq
How to perform full end-to-end entity linking has always been a challenging problem in NLP. The typical approach for this is to use a model to detect entities and then employ another model to perform entity disambiguation. And this paper beautifully formulates these two steps…
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