pwc-archive/papers-with-abstracts huggingface dataset:
User, who art thou? User Profiling for Oral Corpus Platforms
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

This contribution presents the background, design and results of a study of users of three oral corpus platforms in Germany. Roughly 5.000 registered users of the Database for Spoken German (DGD), the GeWiss corpus and the corpora of the Hamburg Centre for Language Corpora (HZSK) were asked to participate in a user survey. This quantitative approach was complemented by qualitative interviews with selected users. We briefly introduce the corpus resources involved in the study in section 2. Section 3 describes the methods employed in the user studies. Section 4 summarizes results of the studies focusing on selected key topics. Section 5 attempts a generalization of these results to larger contexts.
Urdu Summary Corpus
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Articles, Document Summarization, Lemmatization, Morphological Analysis, Part-Of-Speech Tagging

Language resources, such as corpora, are important for various natural language processing tasks. Urdu has millions of speakers around the world but it is under-resourced in terms of standard evaluation resources. This paper reports the construction of a benchmark corpus for Urdu summaries (abstracts) to facilitate the development and evaluation of single document summarization systems for Urdu language. In Urdu, space does not always mark word boundary. Therefore, we created two versions of the same corpus. In the first version, words are separated by space. In contrast, proper word boundaries are manually tagged in the second version. We further apply normalization, part-of-speech tagging, morphological analysis, lemmatization, and stemming for the articles and their summaries in both versions. In order to apply these annotations, we re-implemented some NLP tools for Urdu. We provide Urdu Summary Corpus, all these annotations and the needed software tools (as open-source) for researchers to run experiments and to evaluate their work including but not limited to single-document summarization task.
Laughter in French Spontaneous Conversational Dialogs
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

This paper presents a quantitative description of laughter in height 1-hour French spontaneous conversations. The paper includes the raw figures for laughter as well as more details concerning inter-individual variability. It firstly describes to what extent the amount of laughter and their durations varies from speaker to speaker in all dialogs. In a second suite of analyses, this paper compares our corpus with previous analyzed corpora. In a final set of experiments, it presents some facts about overlapping laughs. This paper have quantified these all effects in free-style conversations, for the first time.
A CUP of CoFee: A large Collection of feedback Utterances Provided with communicative function annotations
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

There have been several attempts to annotate communicative functions to utterances of verbal feedback in English previously. Here, we suggest an annotation scheme for verbal and non-verbal feedback utterances in French including the categories base, attitude, previous and visual. The data comprises conversations, maptasks and negotiations from which we extracted ca. 13,000 candidate feedback utterances and gestures. 12 students were recruited for the annotation campaign of ca. 9,500 instances. Each instance was annotated by between 2 and 7 raters. The evaluation of the annotation agreement resulted in an average best-pair kappa of 0.6. While the base category with the values acknowledgement, evaluation, answer, elicit achieve good agreement, this is not the case for the other main categories. The data sets, which also include automatic extractions of lexical, positional and acoustic features, are freely available and will further be used for machine learning classification experiments to analyse the form-function relationship of feedback.
Internet Argument Corpus 2.0: An SQL schema for Dialogic Social Media and the Corpora to go with it
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

Large scale corpora have benefited many areas of research in natural language processing, but until recently, resources for dialogue have lagged behind. Now, with the emergence of large scale social media websites incorporating a threaded dialogue structure, content feedback, and self-annotation (such as stance labeling), there are valuable new corpora available to researchers. In previous work, we released the INTERNET ARGUMENT CORPUS, one of the first larger scale resources available for opinion sharing dialogue. We now release the INTERNET ARGUMENT CORPUS 2.0 (IAC 2.0) in the hope that others will find it as useful as we have. The IAC 2.0 provides more data than IAC 1.0 and organizes it using an extensible, repurposable SQL schema. The database structure in conjunction with the associated code facilitates querying from and combining multiple dialogically structured data sources. The IAC 2.0 schema provides support for forum posts, quotations, markup (bold, italic, etc), and various annotations, including Stanford CoreNLP annotations. We demonstrate the generalizablity of the schema by providing code to import the ConVote corpus.
MARS-Gym: A Gym framework to model, train, and evaluate Recommender Systems for Marketplaces
Authors: Marlesson R. O. Santana, Luckeciano C. Melo, Fernando H. F. Camargo, Bruno Brandão, Anderson Soares, Renan M. Oliveira, Sandor Caetano
Date: 2020-09-30 00:00:00
Tasks: Fairness, Recommendation Systems

Recommender Systems are especially challenging for marketplaces since they must maximize user satisfaction while maintaining the healthiness and fairness of such ecosystems. In this context, we observed a lack of resources to design, train, and evaluate agents that learn by interacting within these environments. For this matter, we propose MARS-Gym, an open-source framework to empower researchers and engineers to quickly build and evaluate Reinforcement Learning agents for recommendations in marketplaces. MARS-Gym addresses the whole development pipeline: data processing, model design and optimization, and multi-sided evaluation. We also provide the implementation of a diverse set of baseline agents, with a metrics-driven analysis of them in the Trivago marketplace dataset, to illustrate how to conduct a holistic assessment using the available metrics of recommendation, off-policy estimation, and fairness. With MARS-Gym, we expect to bridge the gap between academic research and production systems, as well as to facilitate the design of new algorithms and applications.
Modeling and analysis of the effect of COVID-19 on the stock price: V and L-shape recovery
Authors: Ajit Mahata, Anish rai, Om Prakash, Md Nurujjaman
Date: 2020-09-28 00:00:00

The emergence of the COVID-19 pandemic, a new and novel risk factor, leads to the stock price crash due to the investors' rapid and synchronous sell-off. However, within a short period, the quality sectors start recovering from the bottom. A stock price model has been developed during such crises based on the net-fund-flow ($\Psi_t$) due to institutional investors, and financial antifragility ($\phi$) of a company. We assume that during the crash, the stock price fall is independent of the $\phi$. We study the effects of shock lengths and $\phi$ on the stock price during the crises period using the $\Psi_t$ obtained from synthetic and real fund flow data. We observed that the possibility of recovery of stock with $\phi>0$, termed as quality stock, decreases with an increase in shock-length beyond a specific period. A quality stock with higher $\phi$ shows V-shape recovery and outperform others. The shock length and recovery period of quality stock are almost equal that is seen in the Indian market. Financially stressed stocks, i.e., the stocks with $\phi<0$, show L-shape recovery during the pandemic. The stock data and model analysis shows that the investors, in uncertainty like COVID-19, invest in quality stocks to restructure their portfolio to reduce the risk. The study may help the investors to make the right investment decision during a crisis.
No Rumours Please! A Multi-Indic-Lingual Approach for COVID Fake-Tweet Detection
Authors: Debanjana Kar, Mohit Bhardwaj, Suranjana Samanta, Amar Prakash Azad
Date: 2020-10-14 00:00:00
Tasks: Fake News Detection, Misinformation, Rumour Detection, Zero-Shot Learning

The sudden widespread menace created by the present global pandemic COVID-19 has had an unprecedented effect on our lives. Man-kind is going through humongous fear and dependence on social media like never before. Fear inevitably leads to panic, speculations, and the spread of misinformation. Many governments have taken measures to curb the spread of such misinformation for public well being. Besides global measures, to have effective outreach, systems for demographically local languages have an important role to play in this effort. Towards this, we propose an approach to detect fake news about COVID-19 early on from social media, such as tweets, for multiple Indic-Languages besides English. In addition, we also create an annotated dataset of Hindi and Bengali tweet for fake news detection. We propose a BERT based model augmented with additional relevant features extracted from Twitter to identify fake tweets. To expand our approach to multiple Indic languages, we resort to mBERT based model which is fine-tuned over created dataset in Hindi and Bengali. We also propose a zero-shot learning approach to alleviate the data scarcity issue for such low resource languages. Through rigorous experiments, we show that our approach reaches around 89% F-Score in fake tweet detection which supercedes the state-of-the-art (SOTA) results. Moreover, we establish the first benchmark for two Indic-Languages, Hindi and Bengali. Using our annotated data, our model achieves about 79% F-Score in Hindi and 81% F-Score for Bengali Tweets. Our zero-shot model achieves about 81% F-Score in Hindi and 78% F-Score for Bengali Tweets without any annotated data, which clearly indicates the efficacy of our approach.
LiDAM: Semi-Supervised Learning with Localized Domain Adaptation and Iterative Matching
Authors: Qun Liu, Matthew Shreve, Raja Bala
Date: 2020-10-13 00:00:00
Tasks: Domain Adaptation, Semi-Supervised Image Classification

Although data is abundant, data labeling is expensive. Semi-supervised learning methods combine a few labeled samples with a large corpus of unlabeled data to effectively train models. This paper introduces our proposed method LiDAM, a semi-supervised learning approach rooted in both domain adaptation and self-paced learning. LiDAM first performs localized domain shifts to extract better domain-invariant features for the model that results in more accurate clusters and pseudo-labels. These pseudo-labels are then aligned with real class labels in a self-paced fashion using a novel iterative matching technique that is based on majority consistency over high-confidence predictions. Simultaneously, a final classifier is trained to predict ground-truth labels until convergence. LiDAM achieves state-of-the-art performance on the CIFAR-100 dataset, outperforming FixMatch (73.50% vs. 71.82%) when using 2500 labels.
Group-Buying Recommendation for Social E-Commerce
Authors: Jun Zhang, Chen Gao, Depeng Jin, Yong Li
Date: 2020-10-14 00:00:00

Group buying, as an emerging form of purchase in social e-commerce websites, such as Pinduoduo, has recently achieved great success. In this new business model, users, initiator, can launch a group and share products to their social networks, and when there are enough friends, participants, join it, the deal is clinched. Group-buying recommendation for social e-commerce, which recommends an item list when users want to launch a group, plays an important role in the group success ratio and sales. However, designing a personalized recommendation model for group buying is an entirely new problem that is seldom explored. In this work, we take the first step to approach the problem of group-buying recommendation for social e-commerce and develop a GBGCN method (short for Group-Buying Graph Convolutional Network). Considering there are multiple types of behaviors (launch and join) and structured social network data, we first propose to construct directed heterogeneous graphs to represent behavioral data and social networks. We then develop a graph convolutional network model with multi-view embedding propagation, which can extract the complicated high-order graph structure to learn the embeddings. Last, since a failed group-buying implies rich preferences of the initiator and participants, we design a double-pairwise loss function to distill such preference signals. We collect a real-world dataset of group-buying and conduct experiments to evaluate the performance. Empirical results demonstrate that our proposed GBGCN can significantly outperform baseline methods by 2.69%-7.36%. The codes and the dataset are released at https://github.com/Sweetnow/group-buying-recommendation.
Streaming Transformer ASR with Blockwise Synchronous Inference
Authors: Emiru Tsunoo, Yosuke Kashiwagi, Shinji Watanabe
Date: 2020-06-25 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Decoder, Knowledge Distillation, speech-recognition, Speech Recognition

The Transformer self-attention network has recently shown promising performance as an alternative to recurrent neural networks in end-to-end (E2E) automatic speech recognition (ASR) systems. However, Transformer has a drawback in that the entire input sequence is required to compute self-attention. We have proposed a block processing method for the Transformer encoder by introducing a context-aware inheritance mechanism. An additional context embedding vector handed over from the previously processed block helps encode not only local acoustic information but also global linguistic, channel, and speaker attributes. In this paper, we extend block processing towards an entire streaming E2E ASR system without additional training, by introducing a blockwise synchronous decoding process inspired by a neural transducer into the Transformer decoder. We further apply a knowledge distillation technique with which training of the streaming Transformer is guided by the ordinary batch Transformer model. Evaluations of the HKUST and AISHELL-1 Mandarin tasks and LibriSpeech English task show that our proposed streaming Transformer outperforms conventional online approaches including monotonic chunkwise attention (MoChA). We also confirm that the knowledge distillation technique improves the accuracy further. Our streaming ASR models achieve comparable/superior performance to the batch models and other streaming-based transformer methods in all the tasks.
fugashi, a Tool for Tokenizing Japanese in Python
Authors: Paul McCann
Date: 2020-10-14 00:00:00
Proceeding: EMNLP (NLPOSS) 2020 11
Tasks: Multilingual NLP

Recent years have seen an increase in the number of large-scale multilingual NLP projects. However, even in such projects, languages with special processing requirements are often excluded. One such language is Japanese. Japanese is written without spaces, tokenization is non-trivial, and while high quality open source tokenizers exist they can be hard to use and lack English documentation. This paper introduces fugashi, a MeCab wrapper for Python, and gives an introduction to tokenizing Japanese.
Ensembles of Convolutional Neural Networks models for pediatric pneumonia diagnosis
Authors: Helena Liz, Manuel Sánchez-Montañés, Alfredo Tagarro, Sara Domínguez-Rodríguez, Ron Dagan, David Camacho
Date: 2020-09-29 00:00:00
Tasks: Explainable artificial intelligence, Explainable Artificial Intelligence (XAI), Transfer Learning

Pneumonia is a lung infection that causes 15% of childhood mortality, over 800,000 children under five every year, all over the world. This pathology is mainly caused by viruses or bacteria. X-rays imaging analysis is one of the most used methods for pneumonia diagnosis. These clinical images can be analyzed using machine learning methods such as convolutional neural networks (CNN), which learn to extract critical features for the classification. However, the usability of these systems is limited in medicine due to the lack of interpretability, because of these models cannot be used to generate an understandable explanation (from a human-based perspective), about how they have reached those results. Another problem that difficults the impact of this technology is the limited amount of labeled data in many medicine domains. The main contributions of this work are two fold: the first one is the design of a new explainable artificial intelligence (XAI) technique based on combining the individual heatmaps obtained from each model in the ensemble. This allows to overcome the explainability and interpretability problems of the CNN "black boxes", highlighting those areas of the image which are more relevant to generate the classification. The second one is the development of new ensemble deep learning models to classify chest X-rays that allow highly competitive results using small datasets for training. We tested our ensemble model using a small dataset of pediatric X-rays (950 samples) with low quality and anatomical variability (which represents one of the biggest challenges). We also tested other strategies such as single CNNs trained from scratch and transfer learning using CheXNet. Our results show that our ensemble model outperforms these strategies obtaining highly competitive results. Finally, we confirmed the robustness of our approach using another pneumonia diagnosis dataset [1].
Bot and Gender Detection of Twitter Accounts Using Distortion and LSA
Authors: Andrea Bacciu, Massimo La Morgia, Alessandro Mei, Eugenio Nerio Nemmi, Valerio Neri, Julinda Stefa
Date: 2019-07-01 00:00:00
Tasks: Author Profiling, Gender Prediction, Twitter Bot Detection

In this work, we present our approach for the Author Profiling task of
PAN 2019. The task is divided into two sub-problems, bot, and gender detection,
for two different languages: English and Spanish. For each instance of the problem and each language, we address the problem differently. We use an ensemble
architecture to solve the Bot Detection for accounts that write in English and a
single SVM for those who write in Spanish. For the Gender detection we use a
single SVM architecture for both the languages, but we pre-process the tweets in
a different way. Our final models achieve accuracy over the 90% in the bot detection task, while for the gender detection, of 84.17% and 77.61% respectively for
the English and Spanish languages.
Text Classification Using Label Names Only: A Language Model Self-Training Approach
Authors: Yu Meng, Yunyi Zhang, Jiaxin Huang, Chenyan Xiong, Heng Ji, Chao Zhang, Jiawei Han
Date: 2020-10-14 00:00:00
Proceeding: EMNLP 2020 11
Tasks: Classification, Document Classification, General Classification, Language Modeling, Language Modelling, Representation Learning, Sentiment Analysis, Sentiment Classification, text-classification, Text Classification, Zero-Shot Text Classification

Current text classification methods typically require a good number of human-labeled documents as training data, which can be costly and difficult to obtain in real applications. Humans can perform classification without seeing any labeled examples but only based on a small set of words describing the categories to be classified. In this paper, we explore the potential of only using the label name of each class to train classification models on unlabeled data, without using any labeled documents. We use pre-trained neural language models both as general linguistic knowledge sources for category understanding and as representation learning models for document classification. Our method (1) associates semantically related words with the label names, (2) finds category-indicative words and trains the model to predict their implied categories, and (3) generalizes the model via self-training. We show that our model achieves around 90% accuracy on four benchmark datasets including topic and sentiment classification without using any labeled documents but learning from unlabeled data supervised by at most 3 words (1 in most cases) per class as the label name.
On Deep Learning Techniques to Boost Monocular Depth Estimation for Autonomous Navigation
Authors: Raul de Queiroz Mendes, Eduardo Godinho Ribeiro, Nicolas dos Santos Rosa, Valdir Grassi Jr
Date: 2020-10-13 00:00:00
Tasks: Autonomous Navigation, Autonomous Vehicles, Depth Completion, Monocular Depth Estimation, Semantic Segmentation, Surface Normals Estimation, Visual Odometry

Inferring the depth of images is a fundamental inverse problem within the field of Computer Vision since depth information is obtained through 2D images, which can be generated from infinite possibilities of observed real scenes. Benefiting from the progress of Convolutional Neural Networks (CNNs) to explore structural features and spatial image information, Single Image Depth Estimation (SIDE) is often highlighted in scopes of scientific and technological innovation, as this concept provides advantages related to its low implementation cost and robustness to environmental conditions. In the context of autonomous vehicles, state-of-the-art CNNs optimize the SIDE task by producing high-quality depth maps, which are essential during the autonomous navigation process in different locations. However, such networks are usually supervised by sparse and noisy depth data, from Light Detection and Ranging (LiDAR) laser scans, and are carried out at high computational cost, requiring high-performance Graphic Processing Units (GPUs). Therefore, we propose a new lightweight and fast supervised CNN architecture combined with novel feature extraction models which are designed for real-world autonomous navigation. We also introduce an efficient surface normals module, jointly with a simple geometric 2.5D loss function, to solve SIDE problems. We also innovate by incorporating multiple Deep Learning techniques, such as the use of densification algorithms and additional semantic, surface normals and depth information to train our framework. The method introduced in this work focuses on robotic applications in indoor and outdoor environments and its results are evaluated on the competitive and publicly available NYU Depth V2 and KITTI Depth datasets.
Joint Low Dose CT Denoising And Kidney Segmentation
Authors: Mohammad Eslami, Solale Tabarestani, Malek Adjouadi
Date: 2020-07-31 00:00:00
Tasks: Denoising, Generative Adversarial Network, Image Denoising, Image-to-Image Translation, Segmentation, Translation

In this research, both image denoising and kidney segmentation tasks are addressed jointly via one multitask deep convolutional network. This multitasking scheme yields better results for both tasks compared to separate single-task methods. Also, to the best of our knowledge, this is a first time attempt at addressing these joint tasks in low-dose CT scans (LDCT). This new network is a conditional generative adversarial network (C-GAN) and is an extension of the image-to-image translation network. To investigate the generalized nature of the network, two other conventional single task networks are also exploited, including the well-known 2D UNet method for segmentation and the more recently proposed method WGAN for LDCT denoising. Implementation results proved that the proposed method outperforms UNet and WGAN for both tasks.