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From captions to visual concepts and back
TLDR
This paper uses multiple instance learning to train visual detectors for words that commonly occur in captions, including many different parts of speech such as nouns, verbs, and adjectives, and develops a maximum-entropy language model.
Mitigating Unwanted Biases with Adversarial Learning
TLDR
This work presents a framework for mitigating biases concerning demographic groups by including a variable for the group of interest and simultaneously learning a predictor and an adversary, which results in accurate predictions that exhibit less evidence of stereotyping Z.
A Neural Network Approach to Context-Sensitive Generation of Conversational Responses
TLDR
A neural network architecture is used to address sparsity issues that arise when integrating contextual information into classic statistical models, allowing the system to take into account previous dialog utterances.
Model Cards for Model Reporting
TLDR
This work proposes model cards, a framework that can be used to document any trained machine learning model in the application fields of computer vision and natural language processing, and provides cards for two supervised models: One trained to detect smiling faces in images, and one training to detect toxic comments in text.
Visual Storytelling
TLDR
Modelling concrete description as well as figurative and social language, as provided in this dataset and the storytelling task, has the potential to move artificial intelligence from basic understandings of typical visual scenes towards more and more human-like understanding of grounded event structure and subjective expression.
Spoken Language Derived Measures for Detecting Mild Cognitive Impairment
TLDR
The results indicate that using multiple, complementary measures can aid in automatic detection of MCI, and demonstrate a statistically significant improvement in the area under the ROC curve (AUC) when using automatic spoken language derived features in addition to the neuropsychological test scores.
CLPsych 2015 Shared Task: Depression and PTSD on Twitter
This paper presents a summary of the Computational Linguistics and Clinical Psychology (CLPsych) 2015 shared and unshared tasks. These tasks aimed to provide apples-to-apples comparisons of various
Open Domain Targeted Sentiment
TLDR
The intuition behind this work is that sentiment expressed towards an entity, targeted sentiment, may be viewed as a span of sentiment expressed across the entity, and this representation allows us to model sentiment detection as a sequence tagging problem, jointly discovering people and organizations along with whether there is sentiment directed towards them.
Language Models for Image Captioning: The Quirks and What Works
TLDR
By combining key aspects of the ME and RNN methods, this paper achieves a new record performance over previously published results on the benchmark COCO dataset, however, the gains the authors see in BLEU do not translate to human judgments.
Exploring Nearest Neighbor Approaches for Image Captioning
TLDR
A variety of nearest neighbor baseline approaches for image captioning find a set of nearest neighbour images in the training set from which a caption may be borrowed for the query image by finding the caption that best represents the "consensus" of the set of candidate captions gathered from the nearest neighbor images.
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