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