Abstract

Usage of presuppositions in social media and news discourse can be a powerful way to influence the readers as they usually tend to not examine the truth value of the hidden or indirectly expressed information. Fairclough and Wodak (1997) discuss presupposition at a discourse level where some implicit claims are taken for granted in the explicit meaning of a text or utterance. From the Gricean perspective, the presuppositions of a sentence determine the class of contexts in which the sentence could be felicitously uttered. This paper aims to correlate the type of knowledge presupposed in a news article to the bias present in it. We propose a set of guidelines to identify various kinds of presuppositions in news articles and present a dataset consisting of 1050 articles which are annotated for bias (positive, negative or neutral) and the magnitude of presupposition. We introduce a supervised classification approach for detecting bias in political news which significantly outperforms the existing systems.

Abstract

Sarcasm detection, regarded as one of the subproblems of sentiment analysis, is a very typical task because the introduction of sarcastic words can flip the sentiment of the sentence itself. To date, many research works revolve around detecting sarcasm in one single sentence and there is very limited research to detect sarcasm resulting from multiple sentences. Current models used Long Short Term Memory (Hochreiter and Schmidhuber, 1997) (LSTM) variants with or without attention to detect sarcasm in conversations. We showed that the models using state-of-the-art Bidirectional Encoder Representations from Transformers (Devlin et al., 2018) (BERT), to capture syntactic and semantic information across conversation sentences, performed better than the current models. Based on the data analysis, we estimated that the number of sentences in the conversation that can contribute to the sarcasm and the results agrees to this estimation. We also perform a comparative study of our different versions of BERT-based model with other variants of LSTM model and XLNet (Yang et al., 2019) (both using the estimated number of conversation sentences) and find out that BERT-based models outperformed them.

Abstract

Active learning is an important technique to reduce the number of labeled examples in supervised learning. Active learning for binary classification has been well addressed in machine learning. However, active learning of the reject option classifier remains unaddressed. In this paper, we propose novel algorithms for active learning of reject option classifiers. We develop an active learning algorithm using double ramp loss function. We provide mistake bounds for this algorithm. We also propose a new loss function called double sigmoid loss function for reject option and corresponding active learning algorithm. We offer a convergence guarantee for this algorithm. We provide extensive experimental results to show the effectiveness of the proposed algorithms. The proposed algorithms efficiently reduce the number of label examples required.

Abstract

In this paper, we address the problem of robust learning of multilabel classifiers when the training data has label noise. We consider learning algorithms in the risk-minimization framework. We define what we call symmetric label noise in multi-label settings which is a useful noise model for many random errors in the labeling of data. We prove that risk minimization is robust to symmetric label noise if the loss function satisfies some conditions. We show that Hamming loss and couple of surrogates of Hamming loss satisfy these sufficient conditions and hence are robust. By learning feedforward neural networks on some benchmark multi-label datasets, we provide empirical evidence to illustrate our theoretical results on robust learning of multi-label classifiers under label noise.

Abstract

In this paper, we propose online algorithms for multiclass classification using partial labels. We propose two variants of Perceptron called Avg Perceptron and Max Perceptron to deal with the partially labeled data. We also propose Avg Pegasos and Max Pegasos, which are extensions of the Pegasos algorithm. We also provide mistake bounds for Avg Perceptron and regret bound for Avg Pegasos. We show the effectiveness of the proposed approaches by experimenting on various datasets and comparing them with the standard Perceptron and Pegasos.

Abstract

This paper addresses the problem of multiclass classification with corrupted or noisy bandit feedback. In this setting, the learner may not receive true feedback. Instead, it receives feedback that has been flipped with some non-zero probability. We propose a novel approach to deal with noisy bandit feedback, based on the unbiased estimator technique. We further propose an approach that can efficiently estimate the noise rates, and thus providing an end-to-end framework. The proposed algorithm enjoys mistake bound of the order of O(√T). We provide a theoretical mistake bound for our proposal. We also carry out extensive experiments on several benchmark datasets to demonstrate that our proposed approach successfully learns the underlying classifier even using noisy bandit feedbacks.

Abstract

Monitoring of wildlife is very important for maintaining sustainability of environment. In this paper, we pose Wildlife Monitoring as a Cooperative Target Observation (CTO) problem and propose a Multi Criteria Decision Analysis (MCDA) based algorithm named MCDA-CTO, to maximize the observation of different animal species by Unmanned Aerial Vehicles (UAVs) and to effectively handle multiple target types and the multiple criteria that arise due to targets and environmental factors, during decision making. UAVs have uncertainty in observation of targets which makes it challenging to develop a high-quality monitoring strategy. We therefore develop monitoring techniques that explicitly take actions to improve belief about the true type of targets being observed. In wildlife monitoring, it is often reasonable to assume that the observers may themselves be a subject of observation by unknown adversaries (poachers). Randomizing the observers actions can therefore help to make the target observation strategy less predictable. We then provide experimental validation which shows that the techniques we develop provide a higher (true positive/true negative) ratio along with better randomization than state of the art approaches.

Abstract

Identifying language information from speech utterance is referred to as spoken language identification. Language Identification (LID) is essential in multilingual speech systems. The performance of LID systems have been studied for various adverse conditions such as background noise, telephonic channel, short utterances, so on. In contrast to these studies, for the first time in the literature, the present work investigated the impact of emotional speech on language identification. In this work, different emotional speech databases have been pooled to create the experimental setup. Additionally, state-of-art i-vectors, timedelay neural networks, long short term memory, and deep neural network x-vector systems have been considered to build the LID systems. Performance of the LID system has been evaluated for speech utterances of different emotions in terms of equal error rate and Cavg. The results of the study indicate that the speech utterances of anger and happy emotions degrades performance of LID systems more compared to the neutral and sad emotions. Index Terms—Language Identification, i-vector, TDNN, LSTM, DNN x-vector

Abstract

The objectives of the dysarthria assessment are to discriminate dysarthric speech from normal speech, to estimate the severity of dysarthria in terms of the dysarthric speech intelligibility, and to find the motor speech subsystem which causes defects in speech production. In this work, analytic phase features are investigated for the objective assessment of dysarthria. In this connection, the importance of analytic phase in speech intelligibility is studied by employing phase modification schemes. The current investigation on the analytic phase, proposed a novel approach to estimate the instantaneous frequency components from the speech signal, by using single frequency filtering technique. In this study, dysarthric speech detection and intelligibility assessment systems are developed by using UA-Speech database. The efficiency of the analytic phase features is compared with state-of-the-art spectral features. The proposed features outperformed the magnitude and group delay features and shown a classification accuracies of 95.61% and 64.47% for dysarthric speech detection and intelligibility assessment tasks, respectively. The fusion of the evidence obtained from the analytic phase and magnitude spectral features revealed the complementary nature of analytic phase features.

Abstract

Hypernasality is an abnormality in speech production observed in subjects with craniofacial anomalies like cleft lip and palate (CLP). Detection and assessment of hypernasality is a primary step in the clinical diagnosis of individuals with CLP. Existing methods explore the short-term spectral information from speech to assess hy-pernasality. The present work examines long-term average spectral (LTAS) features obtained from speech to detect and assess hyper-nasality. This work proposes single frequency filter bank based long-term average spectral (SFFB-LTAS) features for hypernasality detection and assessment. The SFFB is used to extract long-term average spectra with a good spectral resolution. The experiments are carried out using NMCPC-CLP database collected from 41 speakers with CLP and 32 speakers without CLP. The experimental results show that, SFFB-LTAS features performed better compared to state-of-art spectral and prosody features. The proposed systems for the detection and assessment of hypernasality have shown classification accuracy of 89% and 82.1%, respectively.