Search Results for author:
Found 11 papers, 6 papers with code
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.
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.
UniCausal: Unified Benchmark and Repository for Causal Text Mining
Current causal text mining datasets vary in objectives, data coverage, and annotation schemes.
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.
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.
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.
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.
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.
Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2023): Workshop and Shared Task Report
We provide a summary of the sixth edition of the CASE workshop that is held in the scope of RANLP 2023.
Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and Understanding -- A Survey
Recent breakthroughs in large language modeling have facilitated rigorous exploration of their application in diverse tasks related to tabular data modeling, such as prediction, tabular data synthesis, question answering, and table understanding.
PHAnToM: Personality Has An Effect on Theory-of-Mind Reasoning in Large Language Models
Drawing inspiration from psychological research on the links between certain personality traits and Theory-of-Mind (ToM) reasoning, and from prompt engineering research on the hyper-sensitivity of prompts in affecting LLMs capabilities, this study investigates how inducing personalities in LLMs using prompts affects their ToM reasoning capabilities.
