Belinda Z. Li


2020

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Active Learning for Coreference Resolution using Discrete Annotation
Belinda Z. Li | Gabriel Stanovsky | Luke Zettlemoyer
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

We improve upon pairwise annotation for active learning in coreference resolution, by asking annotators to identify mention antecedents if a presented mention pair is deemed not coreferent. This simple modification, when combined with a novel mention clustering algorithm for selecting which examples to label, is much more efficient in terms of the performance obtained per annotation budget. In experiments with existing benchmark coreference datasets, we show that the signal from this additional question leads to significant performance gains per human-annotation hour. Future work can use our annotation protocol to effectively develop coreference models for new domains. Our code is publicly available.

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Efficient One-Pass End-to-End Entity Linking for Questions
Belinda Z. Li | Sewon Min | Srinivasan Iyer | Yashar Mehdad | Wen-tau Yih
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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.