Abstract

Social media sites like Facebook, Twitter, and other microblogging forums have emerged as a platform for people to express their opinions and views on different issues and events. It is often observed that people tend to take a stance; in favor, against or neutral towards a particular topic. The task of assessing the stance taken by the individual became significantly important with the emergence in the usage of online social platforms. Automatic stance detection system understands the user’s stance by analyzing the standalone texts against a target entity. Due to the limited contextual information a single sentence provides, it is challenging to solve this task effectively. In this paper, we introduce a Multi-Task Learning (MTL) based deep neural network architecture for automatically detecting stance present in the code-mixed corpus. We apply our approach on Hindi-English code-mixed corpus against the target entity - “Demonetisation.” Our best model achieved the result with a stance prediction accuracy of 63.2% which is a 4.5% overall accuracy improvement compared to the current supervised classification systems developed using the benchmark dataset for code-mixed data stance detection.

Abstract

We propose bilingual word embeddings based on word2vec and fastText models (CBOW and Skip-gram) to address the problem of Humor detection in Hindi-English code-mixed tweets in combination with deep learning architectures. We focus on deep learning approaches which are not widely used on code-mixed data and analyzed their performance by experimenting with three different neural network models. We propose convolution neural network (CNN) and bidirectional long-short term memory (biLSTM) (with and without Attention) models which take the generated bilingual embeddings as input. We make use of Twitter data to create bilingual word embeddings. All our proposed architectures outperform the state-of-the-art results, and Attention based bidirectional LSTM model achieved an accuracy of 73.6% which is an increment of more than 4% compared to the current stateof-the-art results.

Abstract

Anaphora resolution is a challenging task which has been the interest of NLP researchers for a long time. Traditional resolution techniques like eliminative constraints and weighted preferences were successful in many languages. However, they are ineffective in free word order languages like most South Asian languages. Heuristic and rule-based techniques were typical in these languages, which are constrained to context and domain. In this paper, we venture a new strategy using neural networks for resolving anaphora in human-human dialogues. The architecture chiefly consists of three components, a shallow parser for extracting features, a feature vector generator which produces the word embeddings, and a neural network model which will predict the antecedent mention of an anaphora. The system has been trained and tested on Telugu conversation corpus we generated. Given the advantage of the semantic information in word embeddings and appending actor, gender, number, person and part of plural features the model has reached an F1-score of 86.

Abstract

The content on social media now-a-days includes a huge number of messages both in textual and visual forms that are satirical in nature. Satire in the form of irony, sarcasm, and ridicule to a person, is depicted by memes on social media. Satire detection in text is an active area of research, but in the visual domain it is relatively less explored. The objective of our work is detection of satire in images taken from the popular photo-sharing platform - Flickr. Traditional methods for visual satire detection are based on supervised learning which has a necessary requirement of annotating the data which is a tedious task. In our work, to address this issue, we propose a novel, unsupervised approach which leverages the visual semantics of the images. We provide a study of clustering methods, where the difference between visual semantics of the two classes - satirical and non-satirical - becomes the basis for classification of visual content. Here, we suggest an autoencoder based clustering framework to effectively combine embedded feature learning and clustering assignments for detection of satire.

Abstract

With the growing popularity of social networks, cloud services and online applications, people are becoming concerned about the way companies store their data and the ways in which the data can be applied. Privacy with devices and services operated by the voice are of particular interest. To enable studies in privacy, this paper presents a database which quantifies the experience of privacy users have in spoken communication. We focus on the effect of the acoustic environment on that perception of privacy. Speech signals are recorded in scenarios simulating real-life situations, where the acoustic environment has an effect on the experience of privacy. The acoustic data is complemented with measures of the speakers’ experience of privacy, recorded using a questionnaire. The presented corpus enables studies in how acoustic environments affect peoples’ experience of privacy, which in turn, can be used to develop speech operated applications which are respectful of their right to privacy. Index Terms: Experience of privacy, speech interfaces, speech corpus, acoustic environment, right to privacy

Abstract

In this paper, we propose a novel mixture of expert architecture for learning polyhedral classifiers. We learn the parameters of the classifier using an expectation maximization algorithm. We derive the generalization bounds of the proposed approach. Through extensive simulation study, we show that the proposed method performs comparably to other state-of-the-art approaches.

Abstract

Decentralized MDPs (Dec-MDPs) provide a rigorous framework for collaborative multi-agent sequential decisionmaking under uncertainty. However, their computational complexity limits the practical impact. To address this, we focus on a class of Dec-MDPs consisting of independent collaborating agents that are tied together through a global reward function that depends upon their entire histories of states and actions to accomplish joint tasks. To overcome scalability barrier, our main contributions are:(a) We propose a new actor-critic based Reinforcement Learning (RL) approach for event-based Dec-MDPs using successor features (SF) which is a value function representation that decouples the dynamics of the environment from the rewards;(b) We then present Dec-ESR (Decentralized Event based Successor Representation) which generalizes learning for event-based Dec-MDPs using SF within an end-to-end deep RL framework;(c) We also show that Dec-ESR allows useful transfer of information on related but different tasks, hence bootstraps the learning for faster convergence on new tasks;(d) For validation purposes, we test our approach on a large multi-agent coverage problem which models schedule coordination of agents in a real urban subway network and achieves better quality solutions than previous best approaches.

Abstract

One of the major drawbacks of RL is the low sample efficiency of the learning algorithms. In many cases domain expertise can help to mitigate this effect. Teacher-Student framework is one such paradigm, where a more experienced agent (teacher) upon being queried helps to accelerate the student’s learning by providing advice on the action to take in a given state. Real world teachers not only provide the action to take in a given state but also provide a more informative signal using the synthesis of knowledge they may have gained with experience. With this motivation, we propose a richer advising framework where the teacher augments the student’s knowledge by also providing the expected long term reward of following that action. The student can then use this value to steadily guide its Q-Network in the correct direction which can lead to a quicker convergence. To help student relive the advices received throughout its learning, we introduce an additional memory called the Advice Replay Memory (ARM). Results show that a student following our approach (a) is able to exploit the environment better, and (b) has a steeper learning curve.

Abstract

This paper describes the Neural Machine Translation systems used by IIIT Hyderabad (CVIT-MT) for the translation tasks part of WAT-2019. We participated in tasks pertaining to Indian languages and submitted results for English-Hindi, Hindi-English, English-Tamil and Tamil-English language pairs. We employ Transformer architecture experimenting with multilingual models and methods for low-resource languages.

Abstract

Chinese scene text reading is one of the most challenging problems in computer vision and has attracted great interest. Different from English text, Chinese has more than 6000 commonly used characters and Chinese characters can be arranged in various layouts with numerous fonts. The Chinese signboards in street view are a good choice for Chinese scene text images since they have different backgrounds, fonts and layouts. We organized a competition called ICDAR2019-ReCTS, which mainly focuses on reading Chinese text on signboard. This report presents the final results of the competition. A large-scale dataset of 25,000 annotated signboard images, in which all the text lines and characters are annotated with locations and transcriptions, were released. Four tasks, namely character recognition, text line recognition, text line detection and end-to-end recognition were set up. Besides, considering the Chinese text ambiguity issue, we proposed a multi ground truth (multi-GT) evaluation method to make evaluation fairer. The competition started on March 1, 2019 and ended on April 30, 2019. 262 submissions from 46 teams are received. Most of the participants come from universities, research institutes, and tech companies in China. There are also some participants from the United States, Australia, Singapore, and Korea. 21 teams submit results for Task 1, 23 teams submit results for Task 2, 24 teams submit results for Task 3, and 13 teams submit results for Task 4. The official website for the competition is http://rrc.cvc.uab.es/?ch=12.