A pluggable auxiliary sentiment and dependency-path supervision module improves F1 for most tested relation extraction models on REFinD and TACRED.
iMETRE: Incorporating Markers of Entity Types for Relation Extraction
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
Sentence-level relation extraction (RE) aims to identify the relationship between 2 entities given a contextual sentence. While there have been many attempts to solve this problem, the current solutions have a lot of room to improve. In this paper, we approach the task of relationship extraction in the financial dataset REFinD. Our approach incorporates typed entity markers representations and various models finetuned on the dataset, which has allowed us to achieve an F1 score of 69.65% on the validation set. Through this paper, we discuss various approaches and possible limitations.
citation-role summary
background 1
citation-polarity summary
fields
cs.CL 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
background 1representative citing papers
citing papers explorer
-
A Pluggable Multi-Task Learning Framework for Sentiment-Aware Financial Relation Extraction
A pluggable auxiliary sentiment and dependency-path supervision module improves F1 for most tested relation extraction models on REFinD and TACRED.