pwc-archive/papers-with-abstracts huggingface dataset:
Semantic search with domain-specific word-embedding and production monitoring in Fintech
Authors: Mojtaba Farmanbar, Nikki Van Ommeren, Boyang Zhao
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Information Retrieval, Management, Retrieval

We present an end-to-end information retrieval system with domain-specific custom language models for accurate search terms expansion. The text mining pipeline tackles several challenges faced in an industry-setting, including multi-lingual jargon-rich unstructured text and privacy compliance. Combined with a novel statistical approach for word embedding evaluations, the models can be monitored in a production setting. Our approach is used in the real world in risk management in the financial sector and has wide applicability to other domains.
CogniVal in Action: An Interface for Customizable Cognitive Word Embedding Evaluation
Authors: Nora Hollenstein, Adrian van der Lek, Ce Zhang
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Word Embeddings

We demonstrate the functionalities of the new user interface for CogniVal. CogniVal is a framework for the cognitive evaluation of English word embeddings, which evaluates the quality of the embeddings based on their performance to predict human lexical representations from cognitive language processing signals from various sources. In this paper, we present an easy-to-use command line interface for CogniVal with multiple improvements over the original work, including the possibility to evaluate custom embeddings against custom cognitive data sources.
A Multilingual Reading Comprehension System for more than 100 Languages
Authors: Anthony Ferritto, Sara Rosenthal, Mihaela Bornea, Kazi Hasan, Rishav Chakravarti, Salim Roukos, Radu Florian, Avi Sil
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Machine Reading Comprehension, Machine Translation, Question Answering, Reading Comprehension, Translation, XLM-R

This paper presents M-GAAMA, a Multilingual Question Answering architecture and demo system. This is the first multilingual machine reading comprehension (MRC) demo which is able to answer questions in over 100 languages. M-GAAMA answers questions from a given passage in the same or different language. It incorporates several existing multilingual models that can be used interchangeably in the demo such as M-BERT and XLM-R. The M-GAAMA demo also improves language accessibility by incorporating the IBM Watson machine translation widget to provide additional capabilities to the user to see an answer in their desired language. We also show how M-GAAMA can be used in downstream tasks by incorporating it into an END-TO-END-QA system using CFO (Chakravarti et al., 2019). We experiment with our system architecture on the Multi-Lingual Question Answering (MLQA) and the COVID-19 CORD (Wang et al., 2020; Tang et al., 2020) datasets to provide insights into the performance of the system.
XplaiNLI: Explainable Natural Language Inference through Visual Analytics
Authors: Aikaterini-Lida Kalouli, Rita Sevastjanova, Valeria de Paiva, Richard Crouch, Mennatallah El-Assady
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Natural Language Inference

Advances in Natural Language Inference (NLI) have helped us understand what state-of-the-art models really learn and what their generalization power is. Recent research has revealed some heuristics and biases of these models. However, to date, there is no systematic effort to capitalize on those insights through a system that uses these to explain the NLI decisions. To this end, we propose XplaiNLI, an eXplainable, interactive, visualization interface that computes NLI with different methods and provides explanations for the decisions made by the different approaches.
An Online Readability Leveled Arabic Thesaurus
Authors: Zhengyang Jiang, Nizar Habash, Muhamed Al Khalil
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8

This demo paper introduces the online Readability Leveled Arabic Thesaurus interface. For a given user input word, this interface provides the word{'}s possible lemmas, roots, English glosses, related Arabic words and phrases, and readability on a five-level readability scale. This interface builds on and connects multiple existing Arabic resources and processing tools. This one-of-a-kind system enables Arabic speakers and learners to benefit from advances in Arabic computational linguistics technologies. Feedback from users of the system will help the developers to identify lexical coverage gaps and errors. A live link to the demo is available at: http://samer.camel-lab.com/.
TrainX -- Named Entity Linking with Active Sampling and Bi-Encoders
Authors: Tom Oberhauser, Tim Bischoff, Karl Brendel, Maluna Menke, Tobias Klatt, Amy Siu, Felix Alexander Gers, Alexander L{\"o}ser
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Entity Linking

We demonstrate TrainX, a system for Named Entity Linking for medical experts. It combines state-of-the-art entity recognition and linking architectures, such as Flair and fine-tuned Bi-Encoders based on BERT, with an easy-to-use interface for healthcare professionals. We support medical experts in annotating training data by using active sampling strategies to forward informative samples to the annotator. We demonstrate that our model is capable of linking against large knowledge bases, such as UMLS (3.6 million entities), and supporting zero-shot cases, where the linker has never seen the entity before. Those zero-shot capabilities help to mitigate the problem of rare and expensive training data that is a common issue in the medical domain.
BullStop: A Mobile App for Cyberbullying Prevention
Authors: Semiu Salawu, Yulan He, Jo Lumsden
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Blocking

Social media has become the new playground for bullies. Young people are now regularly exposed to a wide range of abuse online. In response to the increasing prevalence of cyberbullying, online social networks have increased efforts to clamp down on online abuse but unfortunately, the nature, complexity and sheer volume of cyberbullying means that many cyberbullying incidents go undetected. BullStop is a mobile app for detecting and preventing cyberbullying and online abuse on social media platforms. It uses deep learning models to identify instances of cyberbullying and can automatically initiate actions such as deleting offensive messages and blocking bullies on behalf of the user. Our system not only achieves impressive prediction results but also demonstrates excellent potential for use in real-world scenarios and is freely available on the Google Play Store.
Annobot: Platform for Annotating and Creating Datasets through Conversation with a Chatbot
Authors: Rafa{\l} Po{\'s}wiata, Micha{\l} Pere{\l}kiewicz
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Chatbot, Machine Translation, Question Answering, Text Summarization, Translation

In this paper, we introduce Annobot: a platform for annotating and creating datasets through conversation with a chatbot. This natural form of interaction has allowed us to create a more accessible and flexible interface, especially for mobile devices. Our solution has a wide range of applications such as data labelling for binary, multi-class/label classification tasks, preparing data for regression problems, or creating sets for issues such as machine translation, question answering or text summarization. Additional features include pre-annotation, active sampling, online learning and real-time inter-annotator agreement. The system is integrated with the popular messaging platform: Facebook Messanger. Usability experiment showed the advantages of the proposed platform compared to other labelling tools. The source code of Annobot is available under the GNU LGPL license at https://github.com/rafalposwiata/annobot.
Arabic Curriculum Analysis
Authors: Hamdy Mubarak, Shimaa Amer, Ahmed Abdelali, Kareem Darwish
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8

Developing a platform that analyzes the content of curricula can help identify their shortcomings and whether they are tailored to specific desired outcomes. In this paper, we present a system to analyze Arabic curricula and provide insights into their content. It allows users to explore word presence, surface-forms used, as well as contrasting statistics between different countries from which the curricula were selected. Also, it provides a facility to grade text in reference to given grade-level and gives users feedback about the complexity or difficulty of words used in a text.
Epistolary Education in 21st Century: A System to Support Composition of E-mails by Students to Superiors in Japanese
Authors: Kenji Ryu, Michal Ptaszynski
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8

E-mail is a communication tool widely used by people of all ages on the Internet today, often in business and formal situations, especially in Japan. Moreover, Japanese E-mail communication has a set of specific rules taught using specialized guidebooks. E-mail literacy education for many Japanese students is typically provided in a traditional, yet inefficient lecture-based way. We propose a system to support Japanese students in writing E-mails to superiors (teachers, job hunting representatives, etc.). We firstly make an investigation into the importance of formal E-mails in Japan, and what is needed to successfully write a formal E-mail. Next, we develop the system with accordance to those rules. Finally, we evaluated the system twofold. The results, although performed on a small number of samples, were generally positive, and clearly indicated additional ways to improve the system.
KVQA: Knowledge-Aware Visual Question Answering
Authors: Sanket Shah, Hyderabad Anand Mishra, Naganand Yadati, Partha Pratim Talukdar
Date: 2019-07-17 00:00:00
Proceeding: AAAI Conference on Artificial Intelligence 2019 7
Tasks: Knowledge Graphs, Question Answering, Visual Question Answering, Visual Question Answering (VQA), World Knowledge

Visual Question Answering (VQA) has emerged as an important problem spanning Computer Vision, Natural Language Processing and Artificial Intelligence (AI). In conventional VQA, one may ask questions about an image which can be answered purely based on its content. For example, given an image with people in it, a typical VQA question may inquire about the number of people in the image. More recently, there is growing interest in answering questions which require commonsense knowledge involving common nouns (e.g., cats, dogs, microphones) present in the image. In spite of this progress, the important problem of answering questions requiring world knowledge about named entities (e.g., Barack Obama, White House, United Nations) in the image has not been addressed in prior research. We address this gap in this paper, and introduce KVQA – the first dataset for the task of (world) knowledge-aware VQA. KVQA consists of 183K question-answer pairs involving more than 18K named entities and 24K images. Questions in this dataset require multi-entity, multi-relation, and multi-hop reasoning over large Knowledge Graphs (KG) to arrive at an answer. To the best of our knowledge, KVQA is the largest dataset for exploring VQA over KG. Further, we also provide baseline performances using state-of-the-art methods on KVQA.
DSOR: A Scalable Statistical Filter for Removing Falling Snow from LiDAR Point Clouds in Severe Winter Weather
Authors: Akhil Kurup, Jeremy Bos
Date: 2021-09-15 00:00:00
Tasks: Autonomous Vehicles

For autonomous vehicles to viably replace human drivers they must contend with inclement weather. Falling rain and snow introduce noise in LiDAR returns resulting in both false positive and false negative object detections. In this article we introduce the Winter Adverse Driving dataSet (WADS) collected in the snow belt region of Michigan's Upper Peninsula. WADS is the first multi-modal dataset featuring dense point-wise labeled sequential LiDAR scans collected in severe winter weather; weather that would cause an experienced driver to alter their driving behavior. We have labelled and will make available over 7 GB or 3.6 billion labelled LiDAR points out of over 26 TB of total LiDAR and camera data collected. We also present the Dynamic Statistical Outlier Removal (DSOR) filter, a statistical PCL-based filter capable or removing snow with a higher recall than the state of the art snow de-noising filter while being 28\% faster. Further, the DSOR filter is shown to have a lower time complexity compared to the state of the art resulting in an improved scalability. Our labeled dataset and DSOR filter will be made available at https://bitbucket.org/autonomymtu/dsor_filter
Multi-Scale Aligned Distillation for Low-Resolution Detection
Authors: Lu Qi, Jason Kuen, Jiuxiang Gu, Zhe Lin, Yi Wang, Yukang Chen, Yanwei Li, Jiaya Jia
Date: 2021-09-14 00:00:00
Proceeding: CVPR 2021 1
Tasks: Knowledge Distillation, object-detection, Object Detection

In instance-level detection tasks (e.g., object detection), reducing input resolution is an easy option to improve runtime efficiency. However, this option traditionally hurts the detection performance much. This paper focuses on boosting the performance of low-resolution models by distilling knowledge from a high- or multi-resolution model. We first identify the challenge of applying knowledge distillation (KD) to teacher and student networks that act on different input resolutions. To tackle it, we explore the idea of spatially aligning feature maps between models of varying input resolutions by shifting feature pyramid positions and introduce aligned multi-scale training to train a multi-scale teacher that can distill its knowledge to a low-resolution student. Further, we propose crossing feature-level fusion to dynamically fuse teacher's multi-resolution features to guide the student better. On several instance-level detection tasks and datasets, the low-resolution models trained via our approach perform competitively with high-resolution models trained via conventional multi-scale training, while outperforming the latter's low-resolution models by 2.1% to 3.6% in terms of mAP. Our code is made publicly available at https://github.com/dvlab-research/MSAD.
A trainable monogenic ConvNet layer robust in front of large contrast changes in image classification
Authors: E. Ulises Moya-Sánchez, Sebastiá Xambo-Descamps, Abraham Sánchez, Sebastián Salazar-Colores, Ulises Cortés
Date: 2021-09-14 00:00:00
Tasks: image-classification, Image Classification, SSIM

Convolutional Neural Networks (ConvNets) at present achieve remarkable performance in image classification tasks. However, current ConvNets cannot guarantee the capabilities of the mammalian visual systems such as invariance to contrast and illumination changes. Some ideas to overcome the illumination and contrast variations usually have to be tuned manually and tend to fail when tested with other types of data degradation. In this context, we present a new bio-inspired {entry} layer, M6, which detects low-level geometric features (lines, edges, and orientations) which are similar to patterns detected by the V1 visual cortex. This new trainable layer is capable of coping with image classification even with large contrast variations. The explanation for this behavior is the monogenic signal geometry, which represents each pixel value in a 3D space using quaternions, a fact that confers a degree of explainability to the networks. We compare M6 with a conventional convolutional layer (C) and a deterministic quaternion local phase layer (Q9). The experimental setup {is designed to evaluate the robustness} of our M6 enriched ConvNet model and includes three architectures, four datasets, three types of contrast degradation (including non-uniform haze degradations). The numerical results reveal that the models with M6 are the most robust in front of any kind of contrast variations. This amounts to a significant enhancement of the C models, which usually have reasonably good performance only when the same training and test degradation are used, except for the case of maximum degradation. Moreover, the Structural Similarity Index Measure (SSIM) is used to analyze and explain the robustness effect of the M6 feature maps under any kind of contrast degradations.
Multi-modal Wound Classification using Wound Image and Location by Deep Neural Network
Authors: D. M. Anisuzzaman, Yash Patel, Behrouz Rostami, Jeffrey Niezgoda, Sandeep Gopalakrishnan, Zeyun Yu
Date: 2021-09-14 00:00:00
Tasks: TAG

Wound classification is an essential step of wound diagnosis. An efficient classifier can assist wound specialists in classifying wound types with less financial and time costs and help them decide an optimal treatment procedure. This study developed a deep neural network-based multi-modal classifier using wound images and their corresponding locations to categorize wound images into multiple classes, including diabetic, pressure, surgical, and venous ulcers. A body map is also developed to prepare the location data, which can help wound specialists tag wound locations more efficiently. Three datasets containing images and their corresponding location information are designed with the help of wound specialists. The multi-modal network is developed by concatenating the image-based and location-based classifier's outputs with some other modifications. The maximum accuracy on mixed-class classifications (containing background and normal skin) varies from 77.33% to 100% on different experiments. The maximum accuracy on wound-class classifications (containing only diabetic, pressure, surgical, and venous) varies from 72.95% to 98.08% on different experiments. The proposed multi-modal network also shows a significant improvement in results from the previous works of literature.
ZFlow: Gated Appearance Flow-based Virtual Try-on with 3D Priors
Authors: Ayush Chopra, Rishabh Jain, Mayur Hemani, Balaji Krishnamurthy
Date: 2021-09-14 00:00:00
Proceeding: ICCV 2021 10
Tasks: SSIM, Virtual Try-on

Image-based virtual try-on involves synthesizing perceptually convincing images of a model wearing a particular garment and has garnered significant research interest due to its immense practical applicability. Recent methods involve a two stage process: i) warping of the garment to align with the model ii) texture fusion of the warped garment and target model to generate the try-on output. Issues arise due to the non-rigid nature of garments and the lack of geometric information about the model or the garment. It often results in improper rendering of granular details. We propose ZFlow, an end-to-end framework, which seeks to alleviate these concerns regarding geometric and textural integrity (such as pose, depth-ordering, skin and neckline reproduction) through a combination of gated aggregation of hierarchical flow estimates termed Gated Appearance Flow, and dense structural priors at various stage of the network. ZFlow achieves state-of-the-art results as observed qualitatively, and on quantitative benchmarks of image quality (PSNR, SSIM, and FID). The paper presents extensive comparisons with other existing solutions including a detailed user study and ablation studies to gauge the effect of each of our contributions on multiple datasets.
PnP-DETR: Towards Efficient Visual Analysis with Transformers
Authors: Tao Wang, Li Yuan, Yunpeng Chen, Jiashi Feng, Shuicheng Yan
Date: 2021-09-15 00:00:00
Proceeding: ICCV 2021 10
Tasks: object-detection, Object Detection, Panoptic Segmentation

Recently, DETR pioneered the solution of vision tasks with transformers, it directly translates the image feature map into the object detection result. Though effective, translating the full feature map can be costly due to redundant computation on some area like the background. In this work, we encapsulate the idea of reducing spatial redundancy into a novel poll and pool (PnP) sampling module, with which we build an end-to-end PnP-DETR architecture that adaptively allocates its computation spatially to be more efficient. Concretely, the PnP module abstracts the image feature map into fine foreground object feature vectors and a small number of coarse background contextual feature vectors. The transformer models information interaction within the fine-coarse feature space and translates the features into the detection result. Moreover, the PnP-augmented model can instantly achieve various desired trade-offs between performance and computation with a single model by varying the sampled feature length, without requiring to train multiple models as existing methods. Thus it offers greater flexibility for deployment in diverse scenarios with varying computation constraint. We further validate the generalizability of the PnP module on panoptic segmentation and the recent transformer-based image recognition model ViT and show consistent efficiency gain. We believe our method makes a step for efficient visual analysis with transformers, wherein spatial redundancy is commonly observed. Code will be available at \url{https://github.com/twangnh/pnp-detr}.
Image Synthesis via Semantic Composition
Authors: Yi Wang, Lu Qi, Ying-Cong Chen, Xiangyu Zhang, Jiaya Jia
Date: 2021-09-15 00:00:00
Proceeding: ICCV 2021 10
Tasks: Image Generation, Semantic Composition

In this paper, we present a novel approach to synthesize realistic images based on their semantic layouts. It hypothesizes that for objects with similar appearance, they share similar representation. Our method establishes dependencies between regions according to their appearance correlation, yielding both spatially variant and associated representations. Conditioning on these features, we propose a dynamic weighted network constructed by spatially conditional computation (with both convolution and normalization). More than preserving semantic distinctions, the given dynamic network strengthens semantic relevance, benefiting global structure and detail synthesis. We demonstrate that our method gives the compelling generation performance qualitatively and quantitatively with extensive experiments on benchmarks.
F-CAM: Full Resolution Class Activation Maps via Guided Parametric Upscaling
Authors: Soufiane Belharbi, Aydin Sarraf, Marco Pedersoli, Ismail Ben Ayed, Luke McCaffrey, Eric Granger
Date: 2021-09-15 00:00:00
Tasks: Object Localization, Weakly-Supervised Object Localization

Class Activation Mapping (CAM) methods have recently gained much attention for weakly-supervised object localization (WSOL) tasks. They allow for CNN visualization and interpretation without training on fully annotated image datasets. CAM methods are typically integrated within off-the-shelf CNN backbones, such as ResNet50. Due to convolution and pooling operations, these backbones yield low resolution CAMs with a down-scaling factor of up to 32, contributing to inaccurate localizations. Interpolation is required to restore full size CAMs, yet it does not consider the statistical properties of objects, such as color and texture, leading to activations with inconsistent boundaries, and inaccurate localizations. As an alternative, we introduce a generic method for parametric upscaling of CAMs that allows constructing accurate full resolution CAMs (F-CAMs). In particular, we propose a trainable decoding architecture that can be connected to any CNN classifier to produce highly accurate CAM localizations. Given an original low resolution CAM, foreground and background pixels are randomly sampled to fine-tune the decoder. Additional priors such as image statistics and size constraints are also considered to expand and refine object boundaries. Extensive experiments, over three CNN backbones and six WSOL baselines on the CUB-200-2011 and OpenImages datasets, indicate that our F-CAM method yields a significant improvement in CAM localization accuracy. F-CAM performance is competitive with state-of-art WSOL methods, yet it requires fewer computations during inference.