pwc-archive/papers-with-abstracts huggingface dataset:
KryptoOracle: A Real-Time Cryptocurrency Price Prediction Platform Using Twitter Sentiments
Authors: Shubhankar Mohapatra, Nauman Ahmed, Paulo Alencar
Date: 2020-02-21 00:00:00
Tasks: Decision Making, Sentiment Analysis

Cryptocurrencies, such as Bitcoin, are becoming increasingly popular, having been widely used as an exchange medium in areas such as financial transaction and asset transfer verification. However, there has been a lack of solutions that can support real-time price prediction to cope with high currency volatility, handle massive heterogeneous data volumes, including social media sentiments, while supporting fault tolerance and persistence in real time, and provide real-time adaptation of learning algorithms to cope with new price and sentiment data. In this paper we introduce KryptoOracle, a novel real-time and adaptive cryptocurrency price prediction platform based on Twitter sentiments. The integrative and modular platform is based on (i) a Spark-based architecture which handles the large volume of incoming data in a persistent and fault tolerant way; (ii) an approach that supports sentiment analysis which can respond to large amounts of natural language processing queries in real time; and (iii) a predictive method grounded on online learning in which a model adapts its weights to cope with new prices and sentiments. Besides providing an architectural design, the paper also describes the KryptoOracle platform implementation and experimental evaluation. Overall, the proposed platform can help accelerate decision-making, uncover new opportunities and provide more timely insights based on the available and ever-larger financial data volume and variety.
Aspect Term Extraction using Graph-based Semi-Supervised Learning
Authors: Gunjan Ansari, Chandni Saxena, Tanvir Ahmad, M. N. Doja
Date: 2020-02-20 00:00:00
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Sentence, Sentiment Analysis, Term Extraction

Aspect based Sentiment Analysis is a major subarea of sentiment analysis. Many supervised and unsupervised approaches have been proposed in the past for detecting and analyzing the sentiment of aspect terms. In this paper, a graph-based semi-supervised learning approach for aspect term extraction is proposed. In this approach, every identified token in the review document is classified as aspect or non-aspect term from a small set of labeled tokens using label spreading algorithm. The k-Nearest Neighbor (kNN) for graph sparsification is employed in the proposed approach to make it more time and memory efficient. The proposed work is further extended to determine the polarity of the opinion words associated with the identified aspect terms in review sentence to generate visual aspect-based summary of review documents. The experimental study is conducted on benchmark and crawled datasets of restaurant and laptop domains with varying value of labeled instances. The results depict that the proposed approach could achieve good result in terms of Precision, Recall and Accuracy with limited availability of labeled data.
SAFE: Similarity-Aware Multi-Modal Fake News Detection
Authors: Xinyi Zhou, Jindi Wu, Reza Zafarani
Date: 2020-02-19 00:00:00
Tasks: Articles, Fake News Detection

Effective detection of fake news has recently attracted significant attention. Current studies have made significant contributions to predicting fake news with less focus on exploiting the relationship (similarity) between the textual and visual information in news articles. Attaching importance to such similarity helps identify fake news stories that, for example, attempt to use irrelevant images to attract readers' attention. In this work, we propose a $\mathsf{S}$imilarity-$\mathsf{A}$ware $\mathsf{F}$ak$\mathsf{E}$ news detection method ($\mathsf{SAFE}$) which investigates multi-modal (textual and visual) information of news articles. First, neural networks are adopted to separately extract textual and visual features for news representation. We further investigate the relationship between the extracted features across modalities. Such representations of news textual and visual information along with their relationship are jointly learned and used to predict fake news. The proposed method facilitates recognizing the falsity of news articles based on their text, images, or their "mismatches." We conduct extensive experiments on large-scale real-world data, which demonstrate the effectiveness of the proposed method.
Investigating an approach for low resource language dataset creation, curation and classification: Setswana and Sepedi
Authors: Vukosi Marivate, Tshephisho Sefara, Vongani Chabalala, Keamogetswe Makhaya, Tumisho Mokgonyane, Rethabile Mokoena, Abiodun Modupe
Date: 2020-02-18 00:00:00
Proceeding: LREC 2020 5
Tasks: Classification, Data Augmentation, General Classification, Topic Classification

The recent advances in Natural Language Processing have been a boon for well-represented languages in terms of available curated data and research resources. One of the challenges for low-resourced languages is clear guidelines on the collection, curation and preparation of datasets for different use-cases. In this work, we take on the task of creation of two datasets that are focused on news headlines (i.e short text) for Setswana and Sepedi and creation of a news topic classification task. We document our work and also present baselines for classification. We investigate an approach on data augmentation, better suited to low resource languages, to improve the performance of the classifiers
A Financial Service Chatbot based on Deep Bidirectional Transformers
Authors: Shi Yu, Yuxin Chen, Hussain Zaidi
Date: 2020-02-17 00:00:00
Tasks: Chatbot, intent-classification, Intent Classification, Language Modeling, Language Modelling, Spelling Correction

We develop a chatbot using Deep Bidirectional Transformer models (BERT) to handle client questions in financial investment customer service. The bot can recognize 381 intents, and decides when to say "I don't know" and escalates irrelevant/uncertain questions to human operators. Our main novel contribution is the discussion about uncertainty measure for BERT, where three different approaches are systematically compared on real problems. We investigated two uncertainty metrics, information entropy and variance of dropout sampling in BERT, followed by mixed-integer programming to optimize decision thresholds. Another novel contribution is the usage of BERT as a language model in automatic spelling correction. Inputs with accidental spelling errors can significantly decrease intent classification performance. The proposed approach combines probabilities from masked language model and word edit distances to find the best corrections for misspelled words. The chatbot and the entire conversational AI system are developed using open-source tools, and deployed within our company's intranet. The proposed approach can be useful for industries seeking similar in-house solutions in their specific business domains. We share all our code and a sample chatbot built on a public dataset on Github.
A Dataset Independent Set of Baselines for Relation Prediction in Argument Mining
Authors: Oana Cocarascu, Elena Cabrio, Serena Villata, Francesca Toni
Date: 2020-02-14 00:00:00
Tasks: Argument Mining, Prediction, Relation, Relation Prediction

Argument Mining is the research area which aims at extracting argument components and predicting argumentative relations (i.e.,support and attack) from text. In particular, numerous approaches have been proposed in the literature to predict the relations holding between the arguments, and application-specific annotated resources were built for this purpose. Despite the fact that these resources have been created to experiment on the same task, the definition of a single relation prediction method to be successfully applied to a significant portion of these datasets is an open research problem in Argument Mining. This means that none of the methods proposed in the literature can be easily ported from one resource to another. In this paper, we address this problem by proposing a set of dataset independent strong neural baselines which obtain homogeneous results on all the datasets proposed in the literature for the argumentative relation prediction task. Thus, our baselines can be employed by the Argument Mining community to compare more effectively how well a method performs on the argumentative relation prediction task.
A Comparative Study of Sequence Classification Models for Privacy Policy Coverage Analysis
Authors: Zachary Lindner
Date: 2020-02-12 00:00:00
Tasks: General Classification

Privacy policies are legal documents that describe how a website will collect, use, and distribute a user's data. Unfortunately, such documents are often overly complicated and filled with legal jargon; making it difficult for users to fully grasp what exactly is being collected and why. Our solution to this problem is to provide users with a coverage analysis of a given website's privacy policy using a wide range of classical machine learning and deep learning techniques. Given a website's privacy policy, the classifier identifies the associated data practice for each logical segment. These data practices/labels are taken directly from the OPP-115 corpus. For example, the data practice "Data Retention" refers to how long a website stores a user's information. The coverage analysis allows users to determine how many of the ten possible data practices are covered, along with identifying the sections that correspond to the data practices of particular interest.
Localized Flood DetectionWith Minimal Labeled Social Media Data Using Transfer Learning
Authors: Neha Singh, Nirmalya Roy, Aryya Gangopadhyay
Date: 2020-02-10 00:00:00
Tasks: Decision Making, General Classification, Language Modeling, Language Modelling, text-classification, Text Classification, Transfer Learning

Social media generates an enormous amount of data on a daily basis but it is very challenging to effectively utilize the data without annotating or labeling it according to the target application. We investigate the problem of localized flood detection using the social sensing model (Twitter) in order to provide an efficient, reliable and accurate flood text classification model with minimal labeled data. This study is important since it can immensely help in providing the flood-related updates and notifications to the city officials for emergency decision making, rescue operations, and early warnings, etc. We propose to perform the text classification using the inductive transfer learning method i.e pre-trained language model ULMFiT and fine-tune it in order to effectively classify the flood-related feeds in any new location. Finally, we show that using very little new labeled data in the target domain we can successfully build an efficient and high performing model for flood detection and analysis with human-generated facts and observations from Twitter.
A Computational Investigation on Denominalization
Authors: Zahra Shekarchi, Yang Xu
Date: 2020-02-13 00:00:00

Language has been a dynamic system and word meanings always have been changed over times. Every time a novel concept or sense is introduced, we need to assign it a word to express it. Also, some changes have happened because the result of a change can be more desirable for humans, or cognitively easier to be used by humans. Finding the patterns of these changes is interesting and can reveal some facts about human cognitive evolution. As we have enough resources for studying this problem, it is a good idea to work on the problem through computational modeling, and that can make the work easier and possible to be studied on large scale. In this work, we want to study the nouns which have been used as verbs after some years of their emergence as nouns and find some commonalities among these nouns. In other words, we are interested in finding what potential requirements are essential for this change.
Mask & Focus: Conversation Modelling by Learning Concepts
Authors: Gaurav Pandey, Dinesh Raghu, Sachindra Joshi
Date: 2020-02-11 00:00:00
Tasks: Machine Translation, Response Generation

Sequence to sequence models attempt to capture the correlation between all the words in the input and output sequences. While this is quite useful for machine translation where the correlation among the words is indeed quite strong, it becomes problematic for conversation modelling where the correlation is often at a much abstract level. In contrast, humans tend to focus on the essential concepts discussed in the conversation context and generate responses accordingly. In this paper, we attempt to mimic this response generating mechanism by learning the essential concepts in the context and response in an unsupervised manner. The proposed model, referred to as Mask \& Focus maps the input context to a sequence of concepts which are then used to generate the response concepts. Together, the context and the response concepts generate the final response. In order to learn context concepts from the training data automatically, we \emph{mask} words in the input and observe the effect of masking on response generation. We train our model to learn those response concepts that have high mutual information with respect to the context concepts, thereby guiding the model to \emph{focus} on the context concepts. Mask \& Focus achieves significant improvement over the existing baselines in several established metrics for dialogues.
Fake News Detection with Different Models
Authors: Sairamvinay Vijayaraghavan, Ye Wang, Zhiyuan Guo, John Voong, Wenda Xu, Armand Nasseri, Jiaru Cai, Linda Li, Kevin Vuong, Eshan Wadhwa
Date: 2020-02-15 00:00:00
Tasks: BIG-bench Machine Learning, Fake News Detection

This is a paper for exploring various different models aiming at developing fake news detection models and we had used certain machine learning algorithms and we had used pretrained algorithms such as TFIDF and CV and W2V as features for processing textual data.
Improving Reliability of Latent Dirichlet Allocation by Assessing Its Stability Using Clustering Techniques on Replicated Runs
Authors: Jonas Rieger, Lars Koppers, Carsten Jentsch, Jörg Rahnenführer
Date: 2020-02-14 00:00:00
Tasks: Articles, Clustering

For organizing large text corpora topic modeling provides useful tools. A widely used method is Latent Dirichlet Allocation (LDA), a generative probabilistic model which models single texts in a collection of texts as mixtures of latent topics. The assignments of words to topics rely on initial values such that generally the outcome of LDA is not fully reproducible. In addition, the reassignment via Gibbs Sampling is based on conditional distributions, leading to different results in replicated runs on the same text data. This fact is often neglected in everyday practice. We aim to improve the reliability of LDA results. Therefore, we study the stability of LDA by comparing assignments from replicated runs. We propose to quantify the similarity of two generated topics by a modified Jaccard coefficient. Using such similarities, topics can be clustered. A new pruning algorithm for hierarchical clustering results based on the idea that two LDA runs create pairs of similar topics is proposed. This approach leads to the new measure S-CLOP ({\bf S}imilarity of multiple sets by {\bf C}lustering with {\bf LO}cal {\bf P}runing) for quantifying the stability of LDA models. We discuss some characteristics of this measure and illustrate it with an application to real data consisting of newspaper articles from \textit{USA Today}. Our results show that the measure S-CLOP is useful for assessing the stability of LDA models or any other topic modeling procedure that characterize its topics by word distributions. Based on the newly proposed measure for LDA stability, we propose a method to increase the reliability and hence to improve the reproducibility of empirical findings based on topic modeling. This increase in reliability is obtained by running the LDA several times and taking as prototype the most representative run, that is the LDA run with highest average similarity to all other runs.
Deep Gaussian Process for Crop Yield Prediction Based on Remote Sensing Data
Authors: Jiaxuan You, Xiaocheng Li, Melvin Low, David Lobell, Stefano Ermon
Date: 2017-02-12 00:00:00
Proceeding: AAAI 2017 2017 2
Tasks: Crop Yield Prediction, Dimensionality Reduction, Humanitarian, Representation Learning

Agricultural monitoring, especially in developing countries,
can help prevent famine and support humanitarian efforts.
A central challenge is yield estimation, i.e., predicting crop
yields before harvest.
We introduce a scalable, accurate, and inexpensive method
to predict crop yields using publicly available remote sensing data. Our approach improves existing techniques in three
ways. First, we forego hand-crafted features traditionally
used in the remote sensing community and propose an approach based on modern representation learning ideas. We
also introduce a novel dimensionality reduction technique
that allows us to train a Convolutional Neural Network or
Long-short Term Memory network and automatically learn
useful features even when labeled training data are scarce.
Finally, we incorporate a Gaussian Process component to
explicitly model the spatio-temporal structure of the data
and further improve accuracy. We evaluate our approach on
county-level soybean yield prediction in the U.S. and show
that it outperforms competing techniques.
Mnemonic Descent Method: A recurrent process applied for end-to-end face alignment
Authors: G. Trigeorgis, P. Snape, M. A. Nicolaou, E. Antonakos, S. Zafeiriou
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: Face Alignment, Head Pose Estimation, Pose Estimation, regression

Cascaded regression has recently become the method of choice for solving non-linear least squares problems such as deformable image alignment. Given a sizeable training set, cascaded regression learns a set of generic rules that are sequentially applied to minimise the least squares problem. Despite the success of cascaded regression for problems such as face alignment and head pose estimation, there are several shortcomings arising in the strategies proposed thus far. Specifically,(a) the regressors are learnt independently,(b) the descent directions may cancel one another out and (c) handcrafted features (eg, HoGs, SIFT etc.) are mainly used to drive the cascade, which may be sub-optimal for the task at hand. In this paper, we propose a combined and jointly trained convolutional recurrent neural network architecture that allows the training of an end-to-end to system that attempts to alleviate the aforementioned drawbacks. The recurrent module facilitates the joint optimisation of the regressors by assuming the cascades form a nonlinear dynamical system, in effect fully utilising the information between all cascade levels by introducing a memory unit that shares information across all levels. The convolutional module allows the network to extract features that are specialised for the task at hand and are experimentally shown to outperform hand-crafted features. We show that the application of the proposed architecture for the problem of face alignment results in a strong improvement over the current state-of-the-art.
Webly Supervised Joint Embedding for Cross-Modal lmage-Text Retrieval
Authors: Niluthpol Chowdhury Mithun, Rameswar Panda, Vagelis Papalexakis, Amit K. Roy-Chowdhury
Date: 2018-10-01 00:00:00
Proceeding: Proceedings of the 26th ACM international conference on Multimedia·October 2018 2018 10
Tasks: Cross-Modal Retrieval, Image-text Retrieval, Retrieval, Text Retrieval

Cross-modal retrieval between visual data and natural language description remains a long-standing challenge in multimedia. While recent image-text retrieval methods offer great promise by learning deep representations aligned across modalities, most of these meth-ods are plagued by the issue of training with small-scale datasets covering a limited number of images with ground-truth sentences. Moreover, it is extremely expensive to create a larger dataset by annotating millions of training images with ground-truth sentences and may lead to a biased model. Inspired by the recent success of web-supervised learning in deep neural networks, we capitalize on readily-available web images with noisy annotations to learn robust image-text joint representation. Specifically, our main idea is to leverage web images and corresponding tags, along with fully annotated datasets, in training for learning the visual-semantic joint embedding. We propose a two-stage approach for the task that can augment a typical supervised pair-wise ranking loss based formulation with weakly-annotated web images to learn a more robust visual-semantic embedding. Extensive experiments on two standard benchmark datasets demonstrate that our method achieves a significant performance gain in image-text retrieval compared to state-of-the-art approaches.
3D Human Pose Estimation via Explicit Compositional Depth Maps
Authors: Haiping Wu, Bin Xiao
Date: 2020-02-08 00:00:00
Proceeding: AAAI 2020 2
Tasks: 3D Human Pose Estimation, Pose Estimation

n this work, we tackle the problem of estimating 3D human pose in camera space from a monocular image. First, we propose to use densely-generated limb depth maps to ease the learning of body joints depth, which are well aligned with image cues. Then, we design a lifting module from 2D pixel coordinates to 3D camera coordinates which explicitly takes the depth values as inputs, and is aligned with camera perspective projection model. We show our method achieves superior performance on large-scale 3D pose datasets Human3.6M and MPI-INF-3DHP, and sets the new state-of-the-art.
On the Convergence of the Dynamic Inner PCA Algorithm
Authors: Sungho Shin, Alex D. Smith, S. Joe Qin, Victor M. Zavala
Date: 2020-03-12 00:00:00

Dynamic inner principal component analysis (DiPCA) is a powerful method for the analysis of time-dependent multivariate data. DiPCA extracts dynamic latent variables that capture the most dominant temporal trends by solving a large-scale, dense, and nonconvex nonlinear program (NLP). A scalable decomposition algorithm has been recently proposed in the literature to solve these challenging NLPs. The decomposition algorithm performs well in practice but its convergence properties are not well understood. In this work, we show that this algorithm is a specialized variant of a coordinate maximization algorithm. This observation allows us to explain why the decomposition algorithm might work (or not) in practice and can guide improvements. We compare the performance of the decomposition strategies with that of the off-the-shelf solver Ipopt. The results show that decomposition is more scalable and, surprisingly, delivers higher quality solutions.