Search Results for author:
Found 7 papers, 6 papers with code
Language Model Decoding as Likelihood-Utility Alignment
Specifically, by analyzing the correlation between the likelihood and the utility of predictions across a diverse set of tasks, we provide empirical evidence supporting the proposed taxonomy and a set of principles to structure reasoning when choosing a decoding algorithm.
Generating Hypothetical Events for Abductive Inference
This work offers the first study of how such knowledge impacts the Abductive NLI task -- which consists in choosing the more likely explanation for given observations.
COINS: Dynamically Generating COntextualized Inference Rules for Narrative Story Completion
Despite recent successes of large pre-trained language models in solving reasoning tasks, their inference capabilities remain opaque.
CO-NNECT: A Framework for Revealing Commonsense Knowledge Paths as Explicitations of Implicit Knowledge in Texts
We conduct evaluations on two argumentative datasets and show that a combination of the two model types generates meaningful, high-quality knowledge paths between sentences that reveal implicit knowledge conveyed in text.
Social Commonsense Reasoning with Multi-Head Knowledge Attention
Notably we are, to the best of our knowledge, the first to demonstrate that a model that learns to perform counterfactual reasoning helps predicting the best explanation in an abductive reasoning task.
Ranking and Selecting Multi-Hop Knowledge Paths to Better Predict Human Needs
To make machines better understand sentiments, research needs to move from polarity identification to understanding the reasons that underlie the expression of sentiment.
Handling Noisy Labels for Robustly Learning from Self-Training Data for Low-Resource Sequence Labeling
In our experiments on Chunking and NER, this approach performs more robustly than the baselines.
