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ForecastQA: A Question Answering Challenge for Event Forecasting with Temporal Text Data
In this work, we aim to formulate a task, construct a dataset, and provide benchmarks for developing methods for event forecasting with large volumes of unstructured text data.
Birds have four legs?! NumerSense: Probing Numerical Commonsense Knowledge of Pre-trained Language Models
Recent works show that pre-trained language models (PTLMs), such as BERT, possess certain commonsense and factual knowledge.
RICA: Evaluating Robust Inference Capabilities Based on Commonsense Axioms
Pre-trained language models (PTLMs) have achieved impressive performance on commonsense inference benchmarks, but their ability to employ commonsense to make robust inferences, which is crucial for effective communications with humans, is debated.
LEAN-LIFE: A Label-Efficient Annotation Framework Towards Learning from Explanation
Successfully training a deep neural network demands a huge corpus of labeled data.
