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ATOMIC: An Atlas of Machine Commonsense for If-Then Reasoning
TLDR
Experimental results demonstrate that multitask models that incorporate the hierarchical structure of if-then relation types lead to more accurate inference compared to models trained in isolation, as measured by both automatic and human evaluation. Expand
COMET: Commonsense Transformers for Automatic Knowledge Graph Construction
TLDR
This investigation reveals promising results when implicit knowledge from deep pre-trained language models is transferred to generate explicit knowledge in commonsense knowledge graphs, and suggests that using generative commonsense models for automatic commonsense KB completion could soon be a plausible alternative to extractive methods. Expand
Defending Against Neural Fake News
TLDR
A model for controllable text generation called Grover, found that best current discriminators can classify neural fake news from real, human-written, news with 73% accuracy, assuming access to a moderate level of training data, and the best defense against Grover turns out to be Grover itself, with 92% accuracy. Expand
Towards Empathetic Open-domain Conversation Models: A New Benchmark and Dataset
TLDR
This work proposes a new benchmark for empathetic dialogue generation and EmpatheticDialogues, a novel dataset of 25k conversations grounded in emotional situations, and presents empirical comparisons of dialogue model adaptations forEmpathetic responding, leveraging existing models or datasets without requiring lengthy re-training of the full model. Expand
Truth of Varying Shades: Analyzing Language in Fake News and Political Fact-Checking
TLDR
Experiments show that while media fact-checking remains to be an open research question, stylistic cues can help determine the truthfulness of text. Expand
Social IQA: Commonsense Reasoning about Social Interactions
TLDR
It is established that Social IQa, the first large-scale benchmark for commonsense reasoning about social situations, is challenging for existing question-answering models based on pretrained language models, compared to human performance (>20% gap). Expand
Abductive Commonsense Reasoning
TLDR
This study introduces a challenge dataset, ART, that consists of over 20k commonsense narrative contexts and 200k explanations, and conceptualizes two new tasks -- Abductive NLI: a multiple-choice question answering task for choosing the more likely explanation, and Abduction NLG: a conditional generation task for explaining given observations in natural language. Expand
Event2Mind: Commonsense Inference on Events, Intents, and Reactions
TLDR
It is demonstrated how commonsense inference on people’s intents and reactions can help unveil the implicit gender inequality prevalent in modern movie scripts. Expand
Modeling Naive Psychology of Characters in Simple Commonsense Stories
TLDR
A new annotation framework is introduced to explain naive psychology of story characters as fully-specified chains of mental states with respect to motivations and emotional reactions and establishes baseline performance on several new tasks, suggesting avenues for future research. Expand
Connotation Frames: A Data-Driven Investigation
TLDR
This work presents models for predicting the connotation frames of verb predicates based on their distributional word representations and the interplay between different types of connotative relations, and investigates the feasibility of obtaining con notative labels through crowdsourcing experiments. Expand
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