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ATOMIC: An Atlas of Machine Commonsense for If-Then Reasoning
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.
Towards Empathetic Open-domain Conversation Models: A New Benchmark and Dataset
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.
COMET: Commonsense Transformers for Automatic Knowledge Graph Construction
- Antoine Bosselut, Hannah Rashkin, Maarten Sap, Chaitanya Malaviya, Asli Celikyilmaz, Yejin Choi
- Computer ScienceACL
- 12 June 2019
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.
Defending Against Neural Fake News
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.
Truth of Varying Shades: Analyzing Language in Fake News and Political Fact-Checking
- Hannah Rashkin, Eunsol Choi, J. Jang, Svitlana Volkova, Yejin Choi
- Computer ScienceEMNLP
- 1 September 2017
Experiments show that while media fact-checking remains to be an open research question, stylistic cues can help determine the truthfulness of text.
Social IQA: Commonsense Reasoning about Social Interactions
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).
Abductive Commonsense Reasoning
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.
Event2Mind: Commonsense Inference on Events, Intents, and Reactions
It is demonstrated how commonsense inference on people’s intents and reactions can help unveil the implicit gender inequality prevalent in modern movie scripts.
Modeling Naive Psychology of Characters in Simple Commonsense Stories
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.
PlotMachines: Outline-Conditioned Generation with Dynamic Plot State Tracking
PlotMachines is presented, a neural narrative model that learns to transform an outline into a coherent story by tracking the dynamic plot states, and is enriched with high-level discourse structure so that the model can learn different styles of writing corresponding to different parts of the narrative.