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On the Use of Linguistic Features for the Evaluation of Generative Dialogue Systems ...
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TorontoCL at CMCL 2021 Shared Task: RoBERTa with Multi-Stage Fine-Tuning for Eye-Tracking Prediction ...
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Quantifying the Task-Specific Information in Text-Based Classifications ...
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How is BERT surprised? Layerwise detection of linguistic anomalies ...
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Semantic coordinates analysis reveals language changes in the AI field ...
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To BERT or Not To BERT: Comparing Speech and Language-based Approaches for Alzheimer's Disease Detection ...
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An information theoretic view on selecting linguistic probes ...
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Examining the rhetorical capacities of neural language models ...
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Lexical Features Are More Vulnerable, Syntactic Features Have More Predictive Power ...
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Representation Learning for Discovering Phonemic Tone Contours ...
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The Effect of Heterogeneous Data for Alzheimer's Disease Detection from Speech ...
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Detecting cognitive impairments by agreeing on interpretations of linguistic features ...
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Abstract:
Linguistic features have shown promising applications for detecting various cognitive impairments. To improve detection accuracies, increasing the amount of data or the number of linguistic features have been two applicable approaches. However, acquiring additional clinical data can be expensive, and hand-crafting features is burdensome. In this paper, we take a third approach, proposing Consensus Networks (CNs), a framework to classify after reaching agreements between modalities. We divide linguistic features into non-overlapping subsets according to their modalities, and let neural networks learn low-dimensional representations that agree with each other. These representations are passed into a classifier network. All neural networks are optimized iteratively. In this paper, we also present two methods that improve the performance of CNs. We then present ablation studies to illustrate the effectiveness of modality division. To understand further what happens in CNs, we visualize the representations during ... : NAACL 2019 ...
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Keyword:
Artificial Intelligence cs.AI; Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://arxiv.org/abs/1808.06570 https://dx.doi.org/10.48550/arxiv.1808.06570
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Dropout during inference as a model for neurological degeneration in an image captioning network ...
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Predicting health inspection results from online restaurant reviews ...
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