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We propose a cascade of neural models that performs sentence classification,phrase recognition, and triple extraction to automatically structure thescholarly contributions of NLP publications. To identify the most importantcontribution sentences in a paper, we used a BERT-based classifier withpositional features (Subtask 1). A BERT-CRF model was used to recognize andcharacterize relevant phrases in contribution sentences (Subtask 2). Wecategorized the triples into several types based on whether and how theirelements were expressed in text, and addressed each type using separateBERT-based classifiers as well as rules (Subtask 3). Our system was officiallyranked second in Phase 1 evaluation and first in both parts of Phase 2evaluation. After fixing a submission error in Pharse 1, our approach yieldsthe best results overall. In this paper, in addition to a system description,we also provide further analysis of our results, highlighting its strengths andlimitations. We make our code publicly available athttps://github.com/Liu-Hy/nlp-contrib-graph.

UIUC_BioNLP at SemEval-2021 Task 11: A Cascade of Neural Models for Structuring Scholarly NLP Contributions