2 research outputs found
COV19IR : COVID-19 Domain Literature Information Retrieval
Increasing number of COVID-19 research literatures cause new challenges in
effective literature screening and COVID-19 domain knowledge aware Information
Retrieval. To tackle the challenges, we demonstrate two tasks along
withsolutions, COVID-19 literature retrieval, and question answering. COVID-19
literature retrieval task screens matching COVID-19 literature documents for
textual user query, and COVID-19 question answering task predicts proper text
fragments from text corpus as the answer of specific COVID-19 related
questions. Based on transformer neural network, we provided solutions to
implement the tasks on CORD-19 dataset, we display some examples to show the
effectiveness of our proposed solutions
FinRED: A Dataset for Relation Extraction in Financial Domain
Relation extraction models trained on a source domain cannot be applied on a
different target domain due to the mismatch between relation sets. In the
current literature, there is no extensive open-source relation extraction
dataset specific to the finance domain. In this paper, we release FinRED, a
relation extraction dataset curated from financial news and earning call
transcripts containing relations from the finance domain. FinRED has been
created by mapping Wikidata triplets using distance supervision method. We
manually annotate the test data to ensure proper evaluation. We also experiment
with various state-of-the-art relation extraction models on this dataset to
create the benchmark. We see a significant drop in their performance on FinRED
compared to the general relation extraction datasets which tells that we need
better models for financial relation extraction.Comment: Accepted at FinWeb at WWW'2