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

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 (Min et al., 2019). 1 * Work done while at Facebook AI.

Awakening Latent Grounding from Pretrained Language Models for Semantic Parsing

“…Our work is also closely related to entity linking and schema linking, which can be viewed as subareas of grounding on specific scenarios. Given an utterance, entity linking aims at finding all mentioned entities in it using a knowledge base as candidate pool (Tan et al, 2017;Chen et al, 2018;Li et al, 2020a), while schema linking tries to find all mentioned schemas related to specific databases (Dong et al, 2019;Lei et al, 2020;Shi et al, 2020). Previous work generally either employed full supervision to train linking models (Li et al, 2020a;Lei et al, 2020;Shi et al, 2020), or treated linking as a minor pre-processing (Yu et al, 2018a;Guo et al, 2019;Lin et al, 2019a) and used heuristic rules to obtain the result.…”

“…Given an utterance, entity linking aims at finding all mentioned entities in it using a knowledge base as candidate pool (Tan et al, 2017;Chen et al, 2018;Li et al, 2020a), while schema linking tries to find all mentioned schemas related to specific databases (Dong et al, 2019;Lei et al, 2020;Shi et al, 2020). Previous work generally either employed full supervision to train linking models (Li et al, 2020a;Lei et al, 2020;Shi et al, 2020), or treated linking as a minor pre-processing (Yu et al, 2018a;Guo et al, 2019;Lin et al, 2019a) and used heuristic rules to obtain the result. Our work is different from them since we optimize the linking model with weak supervision from downstream signals, which is flexible and practicable.…”

“…Despite its success, existing grounding methods mainly relied on heavy manual efforts like high-quality lexicons (Reddy et al, 2016) or adhoc heuristic rules like n-gram matching (Guo et al, 2019), suffering from poor flexibility. To explore more flexible methods, researchers recently tried a data-driven way: they collected grounding annotations as supervision to train grounding models (Li et al, 2020a;Lei et al, 2020;Shi et al, 2020). However, this modeling flexibility in their approaches requires expensive annotations of grounding, which most of the time are not available.…”

“…(2) VCG(Sorokin and Gurevych, 2018) aggregates and mixes contexts of different granularities to perform entity linking. (3) ELQ(Li et al, 2020a) uses a bi-encoder to perform entity linking in one pass, achieving state-of-the-art performance on WebQSP EL and GraphQ EL . VCG and ELQ utilize entity linking supervision in training, while ETA does not.…”

LNN-EL: A Neuro-Symbolic Approach to Short-text Entity Linking

“…(Sakor et al, 2019) also jointly extracts relation spans which aide in overall linking performance. The recent ELQ (Li et al, 2020) extends BLINK to jointly learn mention detection and linking. In contrast, we focus solely on linking and take a different strategy based on combining logic rules with learning.…”