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Found 8 papers, 6 papers with code
NUS-IDS at CASE 2021 Task 1: Improving Multilingual Event Sentence Coreference Identification With Linguistic Information
Event Sentence Coreference Identification (ESCI) aims to cluster event sentences that refer to the same event together for information extraction.
Causal Augmentation for Causal Sentence Classification
Scarcity of annotated causal texts leads to poor robustness when training state-of-the-art language models for causal sentence classification.
Constructing and Interpreting Causal Knowledge Graphs from News
In this work, we propose a methodology to construct causal knowledge graphs (KGs) from news using two steps: (1) Extraction of Causal Relations, and (2) Argument Clustering and Representation into KG.
Event Causality Identification with Causal News Corpus -- Shared Task 3, CASE 2022
The best F1 scores achieved for Subtask 1 and 2 were 86. 19% and 54. 15%, respectively.
UniCausal: Unified Benchmark and Repository for Causal Text Mining
Current causal text mining datasets vary in objectives, data coverage, and annotation schemes.
The Causal News Corpus: Annotating Causal Relations in Event Sentences from News
Leveraging each of these external datasets for training, we achieved up to approximately 64% F1 on the CNC test set without additional fine-tuning.
NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation
Data augmentation is an important component in the robustness evaluation of models in natural language processing (NLP) and in enhancing the diversity of the data they are trained on.
NUS-IDS at FinCausal 2021: Dependency Tree in Graph Neural Network for Better Cause-Effect Span Detection
Automatic identification of cause-effect spans in financial documents is important for causality modelling and understanding reasons that lead to financial events.
