Clickbaits are catchy headlines that are frequently used by social media outlets in order to allure its viewers into clicking them and thus leading them to dubious content. Such venal schemes thrive on exploiting the curiosity of naive social media users, directing traffic to web pages that won't be visited otherwise. In this paper, we propose a novel, semi-supervised classification based approach, that employs attentions sampled from a Gumbel-Softmax distribution to distill contexts that are fairly important in clickbait detection. An additional loss over the attention weights is used to encode prior knowledge. Furthermore, we propose a confidence network that enables learning over weak labels and improves robustness to noisy labels. We show that with merely 30 of strongly labeled samples we can achieve over 97 of the accuracy, of current state of the art methods in clickbait detection.
Some of the most recent approaches that have been successful in modelling curiosity are based on the "novelty" and "surprise" components of a headline. @cite_29 used topic modelling to identify the topic distributions for each headline in the corpus. Distance metrics in the space of propability distributions like KL divergence and Hellinger distance were used as features to express novelty while surprise was primarily modeled using bi-gram frequency counts. Based on these features, an SVM LR classifier was trained on a small section of the training data.
@cite_29
1773803948 2115685217 2805984778 2615461745
This paper presents a statistical model which trains from a corpus annotated with Part Of Speech tags and assigns them to previously unseen text with state of the art accuracy The model can be classi ed as a Maximum Entropy model and simultaneously uses many contextual features to predict the POS tag Furthermore this paper demonstrates the use of specialized fea tures to model di cult tagging decisions discusses the corpus consistency problems discovered during the implementation of these features and proposes a training strategy that mitigates these problems This paper investigates the use of features based on posterior probabilities of subword units such as phonemes. These features are typically transformed when used as inputs for a hidden Markov model with mixture of Gaussians as emission distribution (HMM GMM). In this work, we introduce a novel acoustic model that avoids the Gaussian assumption and directly uses posterior features without any transformation. This model is described by a finite state machine where each state is characterized by a target distribution and the cost function associated to each state is given by the Kullback-Leibler (KL) divergence between its target distribution and the posterior features. Furthermore, hybrid HMM ANN system can be seen as a particular case of this KL-based model where state target distributions are predefined. A recursive training algorithm to estimate the state target distributions is also presented. Recent advances in generative modeling have led to an increased interest in the study of statistical divergences as means of model comparison. Commonly used evaluation methods such as Frechet Inception Distance (FID) correlate well with the perceived quality of samples and they also show sensitivity to dropping modes from the target distribution. However, these metrics are unable to distinguish different failure cases since they inherently only yield one-dimensional scores. We propose a novel definition of precision and recall for distributions which disentangles the divergence into two separate dimensions. The proposed notion is intuitive, retains desirable properties, and naturally leads to an efficient algorithm that can be used to evaluate generative models. We relate this notion to total variation as well as to recent evaluation metrics such as Inception Score and FID. To demonstrate the practical utility of the proposed approach, we perform an empirical study on several variants of Generative Adversarial Networks and the Variational Autoencoder. In an extensive set of experiments, we show that the proposed metric is able to disentangle the quality of samples from the coverage of the target distribution. We consider the task of learning a classifier for semantic segmentation using weak supervision in the form of image labels specifying objects present in the image. Our method uses deep convolutional neural networks (cnns) and adopts an Expectation-Maximization (EM) based approach. We focus on the following three aspects of EM: (i) initialization; (ii) latent posterior estimation (E-step) and (iii) the parameter update (M-step). We show that saliency and attention maps, bottom-up and top-down cues respectively, of images with single objects (simple images) provide highly reliable cues to learn an initialization for the EM. Intuitively, given weak supervisions, we first learn to segment simple images and then move towards the complex ones. Next, for updating the parameters (M step), we propose to minimize the combination of the standard softmax loss and the KL divergence between the latent posterior distribution (obtained using the E-step) and the likelihood given by the cnn. This combination is more robust to wrong predictions made by the E step of the EM algorithm. Extensive experiments and discussions show that our method is very simple and intuitive, and outperforms the state-of-the-art method with a very high margin of 3.7 and 3.9 on the PASCAL VOC12 train and test sets respectively, thus setting new state-of-the-art results.