  Research in Natural Language Processing is making rapid advances, resulting
in the publication of a large number of research papers. Finding relevant
research papers and their contribution to the domain is a challenging problem.
In this paper, we address this challenge via the SemEval 2021 Task 11:
NLPContributionGraph, by developing a system for a research paper
contributions-focused knowledge graph over Natural Language Processing
literature. The task is divided into three sub-tasks: extracting contribution
sentences that show important contributions in the research article, extracting
phrases from the contribution sentences, and predicting the information units
in the research article together with triplet formation from the phrases. The
proposed system is agnostic to the subject domain and can be applied for
building a knowledge graph for any area. We found that transformer-based
language models can significantly improve existing techniques and utilized the
SciBERT-based model. Our first sub-task uses Bidirectional LSTM (BiLSTM)
stacked on top of SciBERT model layers, while the second sub-task uses
Conditional Random Field (CRF) on top of SciBERT with BiLSTM. The third
sub-task uses a combined SciBERT based neural approach with heuristics for
information unit prediction and triplet formation from the phrases. Our system
achieved F1 score of 0.38, 0.63 and 0.76 in end-to-end pipeline testing, phrase
extraction testing and triplet extraction testing respectively.
