Toward Coherent Physical Commonsense Reasoning in Machines
Although lots of state-of-art language models have achieved superhuman performance on accuracy in many natural language understanding tasks, it is important to evaluate coherency in a machine’s reasoning ability. It is significant to advance the alignment in machine reasoning to improve accountability and transparency in human-machine interaction.
TL;DR
- Worked on the project – Toward Coherent Physical Commonsense Reasoning in Machines.
- Assisted in annotating a novel commonsense knowledge benchmark – Tiered Reasoning for Intuitive Physics (TRIP).
- Implemented an algorithm for extracting entity-action pairs from the benchmark data based on dependency parsing.
- Fine-tuned the tiered reasoning system powered by selected language models: BERT, RoBERTa and, DeBERTa.
