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Found 7 papers, 1 papers with code
Low Resource Causal Event Detection from Biomedical Literature
Recognizing causal precedence relations among the chemical interactions in biomedical literature is crucial to understanding the underlying biological mechanisms.
Do Transformers Dream of Inference, or Can Pretrained Generative Models Learn Implicit Inferential Rules?
Large pretrained language models (LM) have been used successfully for multi-hop question answering.
Explainable Verbal Reasoner Plus (EVR+): A Natural Language Reasoning Framework that Supports Diverse Compositional Reasoning
Languages models have been successfully applied to a variety of reasoning tasks in NLP, yet the language models still suffer from compositional generalization.
Better Retrieval May Not Lead to Better Question Answering
Considerable progress has been made recently in open-domain question answering (QA) problems, which require Information Retrieval (IR) and Reading Comprehension (RC).
Explainable Multi-hop Verbal Reasoning Through Internal Monologue
Moreover, models trained on simpler tasks tend to fail when directly tested on more complex problems.
Using the Hammer Only on Nails: A Hybrid Method for Evidence Retrieval for Question Answering
Evidence retrieval is a key component of explainable question answering (QA).
Understanding the Polarity of Events in the Biomedical Literature: Deep Learning vs. Linguistically-informed Methods
An important task in the machine reading of biochemical events expressed in biomedical texts is correctly reading the polarity, i. e., attributing whether the biochemical event is a promotion or an inhibition.
