pwc-archive/papers-with-abstracts huggingface dataset:
Towards an evolutionary-based approach for natural language processing
Authors: Luca Manzoni, Domagoj Jakobovic, Luca Mariot, Stjepan Picek, Mauro Castelli
Date: 2020-04-23 00:00:00
Tasks: Sentence

Tasks related to Natural Language Processing (NLP) have recently been the focus of a large research endeavor by the machine learning community. The increased interest in this area is mainly due to the success of deep learning methods. Genetic Programming (GP), however, was not under the spotlight with respect to NLP tasks. Here, we propose a first proof-of-concept that combines GP with the well established NLP tool word2vec for the next word prediction task. The main idea is that, once words have been moved into a vector space, traditional GP operators can successfully work on vectors, thus producing meaningful words as the output. To assess the suitability of this approach, we perform an experimental evaluation on a set of existing newspaper headlines. Individuals resulting from this (pre-)training phase can be employed as the initial population in other NLP tasks, like sentence generation, which will be the focus of future investigations, possibly employing adversarial co-evolutionary approaches.
CMRNet++: Map and Camera Agnostic Monocular Visual Localization in LiDAR Maps
Authors: Daniele Cattaneo, Domenico Giorgio Sorrenti, Abhinav Valada
Date: 2020-04-20 00:00:00
Tasks: Autonomous Driving, Deep Learning, Visual Localization

Localization is a critically essential and crucial enabler of autonomous robots. While deep learning has made significant strides in many computer vision tasks, it is still yet to make a sizeable impact on improving capabilities of metric visual localization. One of the major hindrances has been the inability of existing Convolutional Neural Network (CNN)-based pose regression methods to generalize to previously unseen places. Our recently introduced CMRNet effectively addresses this limitation by enabling map independent monocular localization in LiDAR-maps. In this paper, we now take it a step further by introducing CMRNet++, which is a significantly more robust model that not only generalizes to new places effectively, but is also independent of the camera parameters. We enable this capability by combining deep learning with geometric techniques, and by moving the metric reasoning outside the learning process. In this way, the weights of the network are not tied to a specific camera. Extensive evaluations of CMRNet++ on three challenging autonomous driving datasets, i.e., KITTI, Argoverse, and Lyft5, show that CMRNet++ outperforms CMRNet as well as other baselines by a large margin. More importantly, for the first-time, we demonstrate the ability of a deep learning approach to accurately localize without any retraining or fine-tuning in a completely new environment and independent of the camera parameters.
Boosting Deep Open World Recognition by Clustering
Authors: Dario Fontanel, Fabio Cermelli, Massimiliano Mancini, Samuel Rota Bulò, Elisa Ricci, Barbara Caputo
Date: 2020-04-20 00:00:00
Tasks: Clustering, Incremental Learning, Open Set Learning

While convolutional neural networks have brought significant advances in robot vision, their ability is often limited to closed world scenarios, where the number of semantic concepts to be recognized is determined by the available training set. Since it is practically impossible to capture all possible semantic concepts present in the real world in a single training set, we need to break the closed world assumption, equipping our robot with the capability to act in an open world. To provide such ability, a robot vision system should be able to (i) identify whether an instance does not belong to the set of known categories (i.e. open set recognition), and (ii) extend its knowledge to learn new classes over time (i.e. incremental learning). In this work, we show how we can boost the performance of deep open world recognition algorithms by means of a new loss formulation enforcing a global to local clustering of class-specific features. In particular, a first loss term, i.e. global clustering, forces the network to map samples closer to the class centroid they belong to while the second one, local clustering, shapes the representation space in such a way that samples of the same class get closer in the representation space while pushing away neighbours belonging to other classes. Moreover, we propose a strategy to learn class-specific rejection thresholds, instead of heuristically estimating a single global threshold, as in previous works. Experiments on RGB-D Object and Core50 datasets show the effectiveness of our approach.
Smart Attendance System Usign CNN
Authors: Shailesh Arya, Hrithik Mesariya, Vishal Parekh
Date: 2020-04-22 00:00:00
Tasks: Face Detection, Face Recognition

The research on the attendance system has been going for a very long time, numerous arrangements have been proposed in the last decade to make this system efficient and less time consuming, but all those systems have several flaws. In this paper, we are introducing a smart and efficient system for attendance using face detection and face recognition. This system can be used to take attendance in colleges or offices using real-time face recognition with the help of the Convolution Neural Network(CNN). The conventional methods like Eigenfaces and Fisher faces are sensitive to lighting, noise, posture, obstruction, illumination etc. Hence, we have used CNN to recognize the face and overcome such difficulties. The attendance records will be updated automatically and stored in an excel sheet as well as in a database. We have used MongoDB as a backend database for attendance records.
Character-level Japanese Text Generation with Attention Mechanism for Chest Radiography Diagnosis
Authors: Kenya Sakka, Kotaro Nakayama, Nisei Kimura, Taiki Inoue, Yusuke Iwasawa, Ryohei Yamaguchi, Yosimasa Kawazoe, Kazuhiko Ohe, Yutaka Matsuo
Date: 2020-04-06 00:00:00
Tasks: Text Generation

Chest radiography is a general method for diagnosing a patient's condition and identifying important information; therefore, radiography is used extensively in routine medical practice in various situations, such as emergency medical care and medical checkup. However, a high level of expertise is required to interpret chest radiographs. Thus, medical specialists spend considerable time in diagnosing such huge numbers of radiographs. In order to solve these problems, methods for generating findings have been proposed. However, the study of generating chest radiograph findings has primarily focused on the English language, and to the best of our knowledge, no studies have studied Japanese data on this subject. There are two challenges involved in generating findings in the Japanese language. The first challenge is that word splitting is difficult because the boundaries of Japanese word are not clear. The second challenge is that there are numerous orthographic variants. For deal with these two challenges, we proposed an end-to-end model that generates Japanese findings at the character-level from chest radiographs. In addition, we introduced the attention mechanism to improve not only the accuracy, but also the interpretation ability of the results. We evaluated the proposed method using a public dataset with Japanese findings. The effectiveness of the proposed method was confirmed using the Bilingual Evaluation Understudy score. And, we were confirmed from the generated findings that the proposed method was able to consider the orthographic variants. Furthermore, we confirmed via visual inspection that the attention mechanism captures the features and positional information of radiographs.
Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images
Authors: Deng-Ping Fan, Tao Zhou, Ge-Peng Ji, Yi Zhou, Geng Chen, Huazhu Fu, Jianbing Shen, Ling Shao
Date: 2020-04-22 00:00:00
Tasks: Camouflage Segmentation, Computed Tomography (CT), Segmentation

Coronavirus Disease 2019 (COVID-19) spread globally in early 2020, causing the world to face an existential health crisis. Automated detection of lung infections from computed tomography (CT) images offers a great potential to augment the traditional healthcare strategy for tackling COVID-19. However, segmenting infected regions from CT slices faces several challenges, including high variation in infection characteristics, and low intensity contrast between infections and normal tissues. Further, collecting a large amount of data is impractical within a short time period, inhibiting the training of a deep model. To address these challenges, a novel COVID-19 Lung Infection Segmentation Deep Network (Inf-Net) is proposed to automatically identify infected regions from chest CT slices. In our Inf-Net, a parallel partial decoder is used to aggregate the high-level features and generate a global map. Then, the implicit reverse attention and explicit edge-attention are utilized to model the boundaries and enhance the representations. Moreover, to alleviate the shortage of labeled data, we present a semi-supervised segmentation framework based on a randomly selected propagation strategy, which only requires a few labeled images and leverages primarily unlabeled data. Our semi-supervised framework can improve the learning ability and achieve a higher performance. Extensive experiments on our COVID-SemiSeg and real CT volumes demonstrate that the proposed Inf-Net outperforms most cutting-edge segmentation models and advances the state-of-the-art performance.
A Wearable Social Interaction Aid for Children with Autism
Authors: Nick Haber, Catalin Voss, Jena Daniels, Peter Washington, Azar Fazel, Aaron Kline, Titas De, Terry Winograd, Carl Feinstein, Dennis P. Wall
Date: 2020-04-19 00:00:00
Tasks: Emotion Recognition, Memorization, Prognosis

With most recent estimates giving an incidence rate of 1 in 68 children in the United States, the autism spectrum disorder (ASD) is a growing public health crisis. Many of these children struggle to make eye contact, recognize facial expressions, and engage in social interactions. Today the standard for treatment of the core autism-related deficits focuses on a form of behavior training known as Applied Behavioral Analysis. To address perceived deficits in expression recognition, ABA approaches routinely involve the use of prompts such as flash cards for repetitive emotion recognition training via memorization. These techniques must be administered by trained practitioners and often at clinical centers that are far outnumbered by and out of reach from the many children and families in need of attention. Waitlists for access are up to 18 months long, and this wait may lead to children regressing down a path of isolation that worsens their long-term prognosis. There is an urgent need to innovate new methods of care delivery that can appropriately empower caregivers of children at risk or with a diagnosis of autism, and that capitalize on mobile tools and wearable devices for use outside of clinical settings.
Uncertainty Modelling in Risk-averse Supply Chain Systems Using Multi-objective Pareto Optimization
Authors: Heerok Banerjee, V. Ganapathy, V. M. Shenbagaraman
Date: 2020-04-24 00:00:00
Tasks: Time Series, Time Series Analysis

One of the arduous tasks in supply chain modelling is to build robust models against irregular variations. During the proliferation of time-series analyses and machine learning models, several modifications were proposed such as acceleration of the classical levenberg-marquardt algorithm, weight decaying and normalization, which introduced an algorithmic optimization approach to this problem. In this paper, we have introduced a novel methodology namely, Pareto Optimization to handle uncertainties and bound the entropy of such uncertainties by explicitly modelling them under some apriori assumptions. We have implemented Pareto Optimization using a genetic approach and compared the results with classical genetic algorithms and Mixed-Integer Linear Programming (MILP) models. Our results yields empirical evidence suggesting that Pareto Optimization can elude such non-deterministic errors and is a formal approach towards producing robust and reactive supply chain models.
A Survey of Document Grounded Dialogue Systems (DGDS)
Authors: Longxuan Ma, Wei-Nan Zhang, Mingda Li, Ting Liu
Date: 2020-04-17 00:00:00
Tasks: Conversational Recommendation, General Classification, Survey

Dialogue system (DS) attracts great attention from industry and academia because of its wide application prospects. Researchers usually divide the DS according to the function. However, many conversations require the DS to switch between different functions. For example, movie discussion can change from chit-chat to QA, the conversational recommendation can transform from chit-chat to recommendation, etc. Therefore, classification according to functions may not be enough to help us appreciate the current development trend. We classify the DS based on background knowledge. Specifically, study the latest DS based on the unstructured document(s). We define Document Grounded Dialogue System (DGDS) as the DS that the dialogues are centering on the given document(s). The DGDS can be used in scenarios such as talking over merchandise against product Manual, commenting on news reports, etc. We believe that extracting unstructured document(s) information is the future trend of the DS because a great amount of human knowledge lies in these document(s). The research of the DGDS not only possesses a broad application prospect but also facilitates AI to better understand human knowledge and natural language. We analyze the classification, architecture, datasets, models, and future development trends of the DGDS, hoping to help researchers in this field.
Fine-tuning Multi-hop Question Answering with Hierarchical Graph Network
Authors: Guanming Xiong
Date: 2020-04-20 00:00:00
Tasks: Graph Neural Network, Language Modeling, Language Modelling, Multi-hop Question Answering, Question Answering

In this paper, we present a two stage model for multi-hop question answering. The first stage is a hierarchical graph network, which is used to reason over multi-hop question and is capable to capture different levels of granularity using the nature structure(i.e., paragraphs, questions, sentences and entities) of documents. The reasoning process is convert to node classify task(i.e., paragraph nodes and sentences nodes). The second stage is a language model fine-tuning task. In a word, stage one use graph neural network to select and concatenate support sentences as one paragraph, and stage two find the answer span in language model fine-tuning paradigm.
Leveraging Personal Navigation Assistant Systems Using Automated Social Media Traffic Reporting
Authors: Xiangpeng Wan, Hakim Ghazzai, Yehia Massoud
Date: 2020-04-21 00:00:00
Tasks: Question Answering

Modern urbanization is demanding smarter technologies to improve a variety of applications in intelligent transportation systems to relieve the increasing amount of vehicular traffic congestion and incidents. Existing incident detection techniques are limited to the use of sensors in the transportation network and hang on human-inputs. Despite of its data abundance, social media is not well-exploited in such context. In this paper, we develop an automated traffic alert system based on Natural Language Processing (NLP) that filters this flood of information and extract important traffic-related bullets. To this end, we employ the fine-tuning Bidirectional Encoder Representations from Transformers (BERT) language embedding model to filter the related traffic information from social media. Then, we apply a question-answering model to extract necessary information characterizing the report event such as its exact location, occurrence time, and nature of the events. We demonstrate the adopted NLP approaches outperform other existing approach and, after effectively training them, we focus on real-world situation and show how the developed approach can, in real-time, extract traffic-related information and automatically convert them into alerts for navigation assistance applications such as navigation apps.
Answer Generation through Unified Memories over Multiple Passages
Authors: Makoto Nakatsuji, Sohei Okui
Date: 2020-04-22 00:00:00
Tasks: Answer Generation, Decoder, Machine Reading Comprehension, Reading Comprehension

Machine reading comprehension methods that generate answers by referring to multiple passages for a question have gained much attention in AI and NLP communities. The current methods, however, do not investigate the relationships among multiple passages in the answer generation process, even though topics correlated among the passages may be answer candidates. Our method, called neural answer Generation through Unified Memories over Multiple Passages (GUM-MP), solves this problem as follows. First, it determines which tokens in the passages are matched to the question. In particular, it investigates matches between tokens in positive passages, which are assigned to the question, and those in negative passages, which are not related to the question. Next, it determines which tokens in the passage are matched to other passages assigned to the same question and at the same time it investigates the topics in which they are matched. Finally, it encodes the token sequences with the above two matching results into unified memories in the passage encoders and learns the answer sequence by using an encoder-decoder with a multiple-pointer-generator mechanism. As a result, GUM-MP can generate answers by pointing to important tokens present across passages. Evaluations indicate that GUM-MP generates much more accurate results than the current models do.
A Review of Winograd Schema Challenge Datasets and Approaches
Authors: Vid Kocijan, Thomas Lukasiewicz, Ernest Davis, Gary Marcus, Leora Morgenstern
Date: 2020-04-23 00:00:00
Tasks: Natural Language Understanding

The Winograd Schema Challenge is both a commonsense reasoning and natural language understanding challenge, introduced as an alternative to the Turing test. A Winograd schema is a pair of sentences differing in one or two words with a highly ambiguous pronoun, resolved differently in the two sentences, that appears to require commonsense knowledge to be resolved correctly. The examples were designed to be easily solvable by humans but difficult for machines, in principle requiring a deep understanding of the content of the text and the situation it describes. This paper reviews existing Winograd Schema Challenge benchmark datasets and approaches that have been published since its introduction.
A Tailored Pre-Training Model for Task-Oriented Dialog Generation
Authors: Jing Gu, Qingyang Wu, Chongruo wu, Weiyan Shi, Zhou Yu
Date: 2020-04-24 00:00:00
Tasks: Knowledge Distillation, Language Modeling, Language Modelling, Position

The recent success of large pre-trained language models such as BERT and GPT-2 has suggested the effectiveness of incorporating language priors in downstream dialog generation tasks. However, the performance of pre-trained models on the dialog task is not as optimal as expected. In this paper, we propose a Pre-trained Role Alternating Language model (PRAL), designed specifically for task-oriented conversational systems. We adopted (Wu et al., 2019) that models two speakers separately. We also design several techniques, such as start position randomization, knowledge distillation, and history discount to improve pre-training performance. We introduce a task-oriented dialog pretraining dataset by cleaning 13 existing data sets. We test PRAL on three different downstream tasks. The results show that PRAL performs better or on par with state-of-the-art methods.
SE-KGE: A Location-Aware Knowledge Graph Embedding Model for Geographic Question Answering and Spatial Semantic Lifting
Authors: Gengchen Mai, Krzysztof Janowicz, Ling Cai, Rui Zhu, Blake Regalia, Bo Yan, Meilin Shi, Ni Lao
Date: 2020-04-25 00:00:00
Tasks: Geographic Question Answering, Graph Embedding, Information Retrieval, Knowledge Graph Embedding, Link Prediction, Question Answering, Retrieval, Spatial Reasoning

Learning knowledge graph (KG) embeddings is an emerging technique for a variety of downstream tasks such as summarization, link prediction, information retrieval, and question answering. However, most existing KG embedding models neglect space and, therefore, do not perform well when applied to (geo)spatial data and tasks. For those models that consider space, most of them primarily rely on some notions of distance. These models suffer from higher computational complexity during training while still losing information beyond the relative distance between entities. In this work, we propose a location-aware KG embedding model called SE-KGE. It directly encodes spatial information such as point coordinates or bounding boxes of geographic entities into the KG embedding space. The resulting model is capable of handling different types of spatial reasoning. We also construct a geographic knowledge graph as well as a set of geographic query-answer pairs called DBGeo to evaluate the performance of SE-KGE in comparison to multiple baselines. Evaluation results show that SE-KGE outperforms these baselines on the DBGeo dataset for geographic logic query answering task. This demonstrates the effectiveness of our spatially-explicit model and the importance of considering the scale of different geographic entities. Finally, we introduce a novel downstream task called spatial semantic lifting which links an arbitrary location in the study area to entities in the KG via some relations. Evaluation on DBGeo shows that our model outperforms the baseline by a substantial margin.
Reevaluating Adversarial Examples in Natural Language
Authors: John X. Morris, Eli Lifland, Jack Lanchantin, Yangfeng Ji, Yanjun Qi
Date: 2020-04-25 00:00:00
Proceeding: Findings of the Association for Computational Linguistics 2020
Tasks: Sentence

State-of-the-art attacks on NLP models lack a shared definition of a what constitutes a successful attack. We distill ideas from past work into a unified framework: a successful natural language adversarial example is a perturbation that fools the model and follows some linguistic constraints. We then analyze the outputs of two state-of-the-art synonym substitution attacks. We find that their perturbations often do not preserve semantics, and 38% introduce grammatical errors. Human surveys reveal that to successfully preserve semantics, we need to significantly increase the minimum cosine similarities between the embeddings of swapped words and between the sentence encodings of original and perturbed sentences.With constraints adjusted to better preserve semantics and grammaticality, the attack success rate drops by over 70 percentage points.
Graph-to-Tree Neural Networks for Learning Structured Input-Output Translation with Applications to Semantic Parsing and Math Word Problem
Authors: Shucheng Li, Lingfei Wu, Shiwei Feng, Fangli Xu, Fengyuan Xu, Sheng Zhong
Date: 2020-04-07 00:00:00
Proceeding: Findings of the Association for Computational Linguistics 2020
Tasks: Decoder, Machine Translation, Math, Math Word Problem Solving, Semantic Parsing, Translation

The celebrated Seq2Seq technique and its numerous variants achieve excellent performance on many tasks such as neural machine translation, semantic parsing, and math word problem solving. However, these models either only consider input objects as sequences while ignoring the important structural information for encoding, or they simply treat output objects as sequence outputs instead of structural objects for decoding. In this paper, we present a novel Graph-to-Tree Neural Networks, namely Graph2Tree consisting of a graph encoder and a hierarchical tree decoder, that encodes an augmented graph-structured input and decodes a tree-structured output. In particular, we investigated our model for solving two problems, neural semantic parsing and math word problem. Our extensive experiments demonstrate that our Graph2Tree model outperforms or matches the performance of other state-of-the-art models on these tasks.