Predicting Directionality in Causal Relations in Text
- URL: http://arxiv.org/abs/2103.13606v1
- Date: Thu, 25 Mar 2021 04:49:01 GMT
- Title: Predicting Directionality in Causal Relations in Text
- Authors: Pedram Hosseini, David A. Broniatowski, Mona Diab
- Abstract summary: SpanBERT performs better than BERT on causal samples with longer span length.
CREST is a framework for unifying a collection of scattered datasets of causal relations.
- Score: 9.313899406300644
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In this work, we test the performance of two bidirectional transformer-based
language models, BERT and SpanBERT, on predicting directionality in causal
pairs in the textual content. Our preliminary results show that predicting
direction for inter-sentence and implicit causal relations is more challenging.
And, SpanBERT performs better than BERT on causal samples with longer span
length. We also introduce CREST which is a framework for unifying a collection
of scattered datasets of causal relations.
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