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Finding Deceptive Opinion Spam by Any Stretch of the Imagination
This work develops and compares three approaches to detecting deceptive opinion spam, and develops a classifier that is nearly 90% accurate on the authors' gold-standard opinion spam dataset, and reveals a relationship between deceptive opinions and imaginative writing. Expand
The Curious Case of Neural Text Degeneration
By sampling text from the dynamic nucleus of the probability distribution, which allows for diversity while effectively truncating the less reliable tail of the distribution, the resulting text better demonstrates the quality of human text, yielding enhanced diversity without sacrificing fluency and coherence. Expand
Neural Motifs: Scene Graph Parsing with Global Context
This work analyzes the role of motifs: regularly appearing substructures in scene graphs and introduces Stacked Motif Networks, a new architecture designed to capture higher order motifs in scene graph graphs that improves on the previous state-of-the-art by an average of 3.6% relative improvement across evaluation settings. Expand
QuAC: Question Answering in Context
QuAC introduces challenges not found in existing machine comprehension datasets: its questions are often more open-ended, unanswerable, or only meaningful within the dialog context, as it shows in a detailed qualitative evaluation. Expand
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. Expand
SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inference
This paper introduces the task of grounded commonsense inference, unifying natural language inference and commonsense reasoning, and proposes Adversarial Filtering (AF), a novel procedure that constructs a de-biased dataset by iteratively training an ensemble of stylistic classifiers, and using them to filter the data. Expand
COMET: Commonsense Transformers for Automatic Knowledge Graph Construction
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
Neural AMR: Sequence-to-Sequence Models for Parsing and Generation
This work presents a novel training procedure that can lift the limitation of the relatively limited amount of labeled data and the non-sequential nature of the AMR graphs, and presents strong evidence that sequence-based AMR models are robust against ordering variations of graph-to-sequence conversions. Expand
From Recognition to Cognition: Visual Commonsense Reasoning
To move towards cognition-level understanding, a new reasoning engine is presented, Recognition to Cognition Networks (R2C), that models the necessary layered inferences for grounding, contextualization, and reasoning. Expand
OpinionFinder: A System for Subjectivity Analysis
OpinionFinder is a system that performs subjectivity analysis, automatically identifying when opinions, sentiments, speculations, and other private states are present in text. Specifically,Expand