pwc-archive/papers-with-abstracts huggingface dataset:
A Transferable Legged Mobile Manipulation Framework Based on Disturbance Predictive Control
Authors: Qingfeng Yao, Jilong Wan, Shuyu Yang, Cong Wang, Linghan Meng, Qifeng Zhang, Donglin Wang
Date: 2022-03-02 00:00:00

Due to their ability to adapt to different terrains, quadruped robots have drawn much attention in the research field of robot learning. Legged mobile manipulation, where a quadruped robot is equipped with a robotic arm, can greatly enhance the performance of the robot in diverse manipulation tasks. Several prior works have investigated legged mobile manipulation from the viewpoint of control theory. However, modeling a unified structure for various robotic arms and quadruped robots is a challenging task. In this paper, we propose a unified framework disturbance predictive control where a reinforcement learning scheme with a latent dynamic adapter is embedded into our proposed low-level controller. Our method can adapt well to various types of robotic arms with a few random motion samples and the experimental results demonstrate the effectiveness of our method.
Understanding The Robustness of Self-supervised Learning Through Topic Modeling
Authors: Zeping Luo, Shiyou Wu, Cindy Weng, Mo Zhou, Rong Ge
Date: 2022-02-02 00:00:00
Tasks: Self-Supervised Learning, Topic Models

Self-supervised learning has significantly improved the performance of many NLP tasks. However, how can self-supervised learning discover useful representations, and why is it better than traditional approaches such as probabilistic models are still largely unknown. In this paper, we focus on the context of topic modeling and highlight a key advantage of self-supervised learning - when applied to data generated by topic models, self-supervised learning can be oblivious to the specific model, and hence is less susceptible to model misspecification. In particular, we prove that commonly used self-supervised objectives based on reconstruction or contrastive samples can both recover useful posterior information for general topic models. Empirically, we show that the same objectives can perform on par with posterior inference using the correct model, while outperforming posterior inference using misspecified models.
GatorTron: A Large Clinical Language Model to Unlock Patient Information from Unstructured Electronic Health Records
Authors: Xi Yang, Aokun Chen, Nima PourNejatian, Hoo Chang Shin, Kaleb E Smith, Christopher Parisien, Colin Compas, Cheryl Martin, Mona G Flores, Ying Zhang, Tanja Magoc, Christopher A Harle, Gloria Lipori, Duane A Mitchell, William R Hogan, Elizabeth A Shenkman, Jiang Bian, Yonghui Wu
Date: 2022-02-02 00:00:00
Tasks: Clinical Concept Extraction, Language Modeling, Language Modelling, Medical Question Answering, Medical Relation Extraction, Natural Language Inference, Question Answering, Relation Extraction, Semantic Textual Similarity, Zero-Shot Learning

There is an increasing interest in developing artificial intelligence (AI) systems to process and interpret electronic health records (EHRs). Natural language processing (NLP) powered by pretrained language models is the key technology for medical AI systems utilizing clinical narratives. However, there are few clinical language models, the largest of which trained in the clinical domain is comparatively small at 110 million parameters (compared with billions of parameters in the general domain). It is not clear how large clinical language models with billions of parameters can help medical AI systems utilize unstructured EHRs. In this study, we develop from scratch a large clinical language model - GatorTron - using >90 billion words of text (including >82 billion words of de-identified clinical text) and systematically evaluate it on 5 clinical NLP tasks including clinical concept extraction, medical relation extraction, semantic textual similarity, natural language inference (NLI), and medical question answering (MQA). We examine how (1) scaling up the number of parameters and (2) scaling up the size of the training data could benefit these NLP tasks. GatorTron models scale up the clinical language model from 110 million to 8.9 billion parameters and improve 5 clinical NLP tasks (e.g., 9.6% and 9.5% improvement in accuracy for NLI and MQA), which can be applied to medical AI systems to improve healthcare delivery. The GatorTron models are publicly available at: https://catalog.ngc.nvidia.com/orgs/nvidia/teams/clara/models/gatortron_og.
Double-Barreled Question Detection at Momentive
Authors: Peng Jiang, Krishna Sumanth Muppalla, Qing Wei, Chidambara Natarajan Gopal, Chun Wang
Date: 2022-02-12 00:00:00
Tasks: Active Learning, BIG-bench Machine Learning, Survey

Momentive offers solutions in market research, customer experience, and enterprise feedback. The technology is gleaned from the billions of real responses to questions asked on the platform. However, people may create biased questions. A double-barreled question (DBQ) is a common type of biased question that asks two aspects in one question. For example, "Do you agree with the statement: The food is yummy, and the service is great.". This DBQ confuses survey respondents because there are two parts in a question. DBQs impact both the survey respondents and the survey owners. Momentive aims to detect DBQs and recommend survey creators to make a change towards gathering high quality unbiased survey data. Previous research work has suggested detecting DBQs by checking the existence of grammatical conjunction. While this is a simple rule-based approach, this method is error-prone because conjunctions can also exist in properly constructed questions. We present an end-to-end machine learning approach for DBQ classification in this work. We handled this imbalanced data using active learning, and compared state-of-the-art embedding algorithms to transform text data into vectors. Furthermore, we proposed a model interpretation technique propagating the vector-level SHAP values to a SHAP value for each word in the questions. We concluded that the word2vec subword embedding with maximum pooling is the optimal word embedding representation in terms of precision and running time in the offline experiments using the survey data at Momentive. The A/B test and production metrics indicate that this model brings a positive change to the business. To the best of our knowledge, this is the first machine learning framework for DBQ detection, and it successfully differentiates Momentive from the competitors. We hope our work sheds light on machine learning approaches for bias question detection.
Assessment of contextualised representations in detecting outcome phrases in clinical trials
Authors: Micheal Abaho, Danushka Bollegala, Paula R Williamson, Susanna Dodd
Date: 2022-02-13 00:00:00
Tasks: Decision Making, Specificity

Automating the recognition of outcomes reported in clinical trials using machine learning has a huge potential of speeding up access to evidence necessary in healthcare decision-making. Prior research has however acknowledged inadequate training corpora as a challenge for the Outcome detection (OD) task. Additionally, several contextualized representations like BERT and ELMO have achieved unparalleled success in detecting various diseases, genes, proteins, and chemicals, however, the same cannot be emphatically stated for outcomes, because these models have been relatively under-tested and studied for the OD task. We introduce "EBM-COMET", a dataset in which 300 PubMed abstracts are expertly annotated for clinical outcomes. Unlike prior related datasets that use arbitrary outcome classifications, we use labels from a taxonomy recently published to standardize outcome classifications. To extract outcomes, we fine-tune a variety of pre-trained contextualized representations, additionally, we use frozen contextualized and context-independent representations in our custom neural model augmented with clinically informed Part-Of-Speech embeddings and a cost-sensitive loss function. We adopt strict evaluation for the trained models by rewarding them for correctly identifying full outcome phrases rather than words within the entities i.e. given an outcome "systolic blood pressure", the models are rewarded a classification score only when they predict all 3 words in sequence, otherwise, they are not rewarded. We observe our best model (BioBERT) achieve 81.5\% F1, 81.3\% sensitivity and 98.0\% specificity. We reach a consensus on which contextualized representations are best suited for detecting outcomes from clinical-trial abstracts. Furthermore, our best model outperforms scores published on the original EBM-NLP dataset leader-board scores.
AI-based Approach for Safety Signals Detection from Social Networks: Application to the Levothyrox Scandal in 2017 on Doctissimo Forum
Authors: Valentin Roche, Jean-Philippe Robert, Hanan Salam
Date: 2022-02-01 00:00:00
Tasks: Pharmacovigilance, Semantic Similarity, Semantic Textual Similarity, Sentiment Analysis

Social media can be an important source of information facilitating the detection of new safety signals in pharmacovigilance. Various approaches have investigated the analysis of social media data using AI such as NLP techniques for detecting adverse drug events. Existing approaches have focused on the extraction and identification of Adverse Drug Reactions, Drug-Drug Interactions and drug misuse. However, non of the works tackled the detection of potential safety signals by taking into account the evolution in time of relevant indicators. Moreover, despite the success of deep learning in various healthcare applications, it was not explored for this task. We propose an AI-based approach for the detection of potential pharmaceutical safety signals from patients' reviews that can be used as part of the pharmacovigilance surveillance process to flag the necessity of an in-depth pharmacovigilance investigation. We focus on the Levothyrox case in France which triggered huge attention from the media following the change of the medication formula, leading to an increase in the frequency of adverse drug reactions normally reported by patients. Our approach is two-fold. (1) We investigate various NLP-based indicators extracted from patients' reviews including words and n-grams frequency, semantic similarity, Adverse Drug Reactions mentions, and sentiment analysis. (2) We propose a deep learning architecture, named Word Cloud Convolutional Neural Network (WC-CNN) which trains a CNN on word clouds extracted from the patients comments. We study the effect of different time resolutions and different NLP pre-processing techniques on the model performance. Our results show that the proposed indicators could be used in the future to effectively detect new safety signals. The WC-CNN model trained on word clouds extracted at monthly resolution outperforms the others with an accuracy of 75%.
Fairness for Text Classification Tasks with Identity Information Data Augmentation Methods
Authors: Mohit Wadhwa, Mohan Bhambhani, Ashvini Jindal, Uma Sawant, Ramanujam Madhavan
Date: 2022-02-04 00:00:00
Tasks: counterfactual, Data Augmentation, Fairness, text-classification, Text Classification, Word Embeddings

Counterfactual fairness methods address the question: How would the prediction change if the sensitive identity attributes referenced in the text instance were different? These methods are entirely based on generating counterfactuals for the given training and test set instances. Counterfactual instances are commonly prepared by replacing sensitive identity terms, i.e., the identity terms present in the instance are replaced with other identity terms that fall under the same sensitive category. Therefore, the efficacy of these methods depends heavily on the quality and comprehensiveness of identity pairs. In this paper, we offer a two-step data augmentation process where (1) the former stage consists of a novel method for preparing a comprehensive list of identity pairs with word embeddings, and (2) the latter consists of leveraging prepared identity pairs list to enhance the training instances by applying three simple operations (namely identity pair replacement, identity term blindness, and identity pair swap). We empirically show that the two-stage augmentation process leads to diverse identity pairs and an enhanced training set, with an improved counterfactual token-based fairness metric score on two well-known text classification tasks.
RNN Transducers for Nested Named Entity Recognition with constraints on alignment for long sequences
Authors: Hagen Soltau, Izhak Shafran, Mingqiu Wang, Laurent El Shafey
Date: 2022-02-08 00:00:00
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Nested Named Entity Recognition, Question Answering

Popular solutions to Named Entity Recognition (NER) include conditional random fields, sequence-to-sequence models, or utilizing the question-answering framework. However, they are not suitable for nested and overlapping spans with large ontologies and for predicting the position of the entities. To fill this gap, we introduce a new model for NER task -- an RNN transducer (RNN-T). These models are trained using paired input and output sequences without explicitly specifying the alignment between them, similar to other seq-to-seq models. RNN-T models learn the alignment using a loss function that sums over all alignments. In NER tasks, however, the alignment between words and target labels are available from the human annotations. We propose a fixed alignment RNN-T model that utilizes the given alignment, while preserving the benefits of RNN-Ts such as modeling output dependencies. As a more general case, we also propose a constrained alignment model where users can specify a relaxation of the given input alignment and the model will learn an alignment within the given constraints. In other words, we propose a family of seq-to-seq models which can leverage alignments between input and target sequences when available. Through empirical experiments on a challenging real-world medical NER task with multiple nested ontologies, we demonstrate that our fixed alignment model outperforms the standard RNN-T model, improving F1-score from 0.70 to 0.74.
Toxic Comments Hunter : Score Severity of Toxic Comments
Authors: Zhichang Wang, Qipeng Zhu
Date: 2022-02-15 00:00:00

The detection and identification of toxic comments are conducive to creating a civilized and harmonious Internet environment. In this experiment, we collected various data sets related to toxic comments. Because of the characteristics of comment data, we perform data cleaning and feature extraction operations on it from different angles to obtain different toxic comment training sets. In terms of model construction, we used the training set to train the models based on TFIDF and finetuned the Bert model separately. Finally, we encapsulated the code into software to score toxic comments in real-time.
Question-Answer Sentence Graph for Joint Modeling Answer Selection
Authors: Roshni G. Iyer, Thuy Vu, Alessandro Moschitti, Yizhou Sun
Date: 2022-02-16 00:00:00
Tasks: Answer Selection, Question Answering, Retrieval, Sentence

This research studies graph-based approaches for Answer Sentence Selection (AS2), an essential component for retrieval-based Question Answering (QA) systems. During offline learning, our model constructs a small-scale relevant training graph per question in an unsupervised manner, and integrates with Graph Neural Networks. Graph nodes are question sentence to answer sentence pairs. We train and integrate state-of-the-art (SOTA) models for computing scores between question-question, question-answer, and answer-answer pairs, and use thresholding on relevance scores for creating graph edges. Online inference is then performed to solve the AS2 task on unseen queries. Experiments on two well-known academic benchmarks and a real-world dataset show that our approach consistently outperforms SOTA QA baseline models.
Fine-Tuning Approach for Arabic Offensive Language Detection System: BERT-Based Model
Authors: Fatemah Husain, Ozlem Uzuner
Date: 2022-02-07 00:00:00
Tasks: Transfer Learning

The problem of online offensive language limits the health and security of online users. It is essential to apply the latest state-of-the-art techniques in developing a system to detect online offensive language and to ensure social justice to the online communities. Our study investigates the effects of fine-tuning across several Arabic offensive language datasets. We develop multiple classifiers that use four datasets individually and in combination in order to gain knowledge about online Arabic offensive content and classify users comments accordingly. Our results demonstrate the limited effects of transfer learning on the classifiers performance, particularly for highly dialectal comments.
LMN at SemEval-2022 Task 11: A Transformer-based System for English Named Entity Recognition
Authors: Ngoc Minh Lai
Date: 2022-02-13 00:00:00
Tasks: Data Augmentation, Entity Linking, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

Processing complex and ambiguous named entities is a challenging research problem, but it has not received sufficient attention from the natural language processing community. In this short paper, we present our participation in the English track of SemEval-2022 Task 11: Multilingual Complex Named Entity Recognition. Inspired by the recent advances in pretrained Transformer language models, we propose a simple yet effective Transformer-based baseline for the task. Despite its simplicity, our proposed approach shows competitive results in the leaderboard as we ranked 12 over 30 teams. Our system achieved a macro F1 score of 72.50% on the held-out test set. We have also explored a data augmentation approach using entity linking. While the approach does not improve the final performance, we also discuss it in this paper.
Counter Hate Speech in Social Media: A Survey
Authors: Dana Alsagheer, Hadi Mansourifar, Weidong Shi
Date: 2022-02-21 00:00:00
Tasks: Survey

With the high prevalence of offensive language against minorities in social media, counter-hate speeches (CHS) generation is considered an automatic way of tackling this challenge. The CHS is supposed to appear as a third voice to educate people and keep the social [red lines bold] without limiting the principles of freedom of speech. In this paper, we review the most important research in the past and present with a main focus on methodologies, collected datasets and statistical analysis CHS's impact on social media. The CHS generation is based on the optimistic assumption that any attempt to intervene the hate speech in social media can play a positive role in this context. Beyond that, previous works ignored the investigation of the sequence of comments before and after the CHS. However, the positive impact is not guaranteed, as shown in some previous works. To the best of our knowledge, no attempt has been made to survey the related work to compare the past research in terms of CHS's impact on social media. We take the first step in this direction by providing a comprehensive review on related works and categorizing them based on different factors including impact, methodology, data source, etc.
Follow the Water: Finding Water, Snow and Clouds on Terrestrial Exoplanets with Photometry and Machine Learning
Authors: Dang Pham, Lisa Kaltenegger
Date: 2022-03-08 00:00:00
Tasks: BIG-bench Machine Learning, Retrieval

All life on Earth needs water. NASA's quest to follow the water links water to the search for life in the cosmos. Telescopes like JWST and mission concepts like HabEx, LUVOIR and Origins are designed to characterise rocky exoplanets spectroscopically. However, spectroscopy remains time-intensive and therefore, initial characterisation is critical to prioritisation of targets. Here, we study machine learning as a tool to assess water's existence through broadband-filter reflected photometric flux on Earth-like exoplanets in three forms: seawater, water-clouds and snow; based on 53,130 spectra of cold, Earth-like planets with 6 major surfaces. XGBoost, a well-known machine learning algorithm, achieves over 90\% balanced accuracy in detecting the existence of snow or clouds for S/N$\gtrsim 20$, and 70\% for liquid seawater for S/N $\gtrsim 30$. Finally, we perform mock Bayesian analysis with Markov-chain Monte Carlo with five filters identified to derive exact surface compositions to test for retrieval feasibility. The results show that the use of machine learning to identify water on the surface of exoplanets from broadband-filter photometry provides a promising initial characterisation tool of water in different forms. Planned small and large telescope missions could use this to aid their prioritisation of targets for time-intense follow-up observations.
Shadows can be Dangerous: Stealthy and Effective Physical-world Adversarial Attack by Natural Phenomenon
Authors: Yiqi Zhong, Xianming Liu, Deming Zhai, Junjun Jiang, Xiangyang Ji
Date: 2022-03-08 00:00:00
Proceeding: CVPR 2022 1
Tasks: Adversarial Attack, Traffic Sign Recognition, valid

Estimating the risk level of adversarial examples is essential for safely deploying machine learning models in the real world. One popular approach for physical-world attacks is to adopt the "sticker-pasting" strategy, which however suffers from some limitations, including difficulties in access to the target or printing by valid colors. A new type of non-invasive attacks emerged recently, which attempt to cast perturbation onto the target by optics based tools, such as laser beam and projector. However, the added optical patterns are artificial but not natural. Thus, they are still conspicuous and attention-grabbed, and can be easily noticed by humans. In this paper, we study a new type of optical adversarial examples, in which the perturbations are generated by a very common natural phenomenon, shadow, to achieve naturalistic and stealthy physical-world adversarial attack under the black-box setting. We extensively evaluate the effectiveness of this new attack on both simulated and real-world environments. Experimental results on traffic sign recognition demonstrate that our algorithm can generate adversarial examples effectively, reaching 98.23% and 90.47% success rates on LISA and GTSRB test sets respectively, while continuously misleading a moving camera over 95% of the time in real-world scenarios. We also offer discussions about the limitations and the defense mechanism of this attack.
Neural Face Identification in a 2D Wireframe Projection of a Manifold Object
Authors: Kehan Wang, Jia Zheng, Zihan Zhou
Date: 2022-03-08 00:00:00
Proceeding: CVPR 2022 1
Tasks: 3D Object Reconstruction, Object, Object Reconstruction

In computer-aided design (CAD) systems, 2D line drawings are commonly used to illustrate 3D object designs. To reconstruct the 3D models depicted by a single 2D line drawing, an important key is finding the edge loops in the line drawing which correspond to the actual faces of the 3D object. In this paper, we approach the classical problem of face identification from a novel data-driven point of view. We cast it as a sequence generation problem: starting from an arbitrary edge, we adopt a variant of the popular Transformer model to predict the edges associated with the same face in a natural order. This allows us to avoid searching the space of all possible edge loops with various hand-crafted rules and heuristics as most existing methods do, deal with challenging cases such as curved surfaces and nested edge loops, and leverage additional cues such as face types. We further discuss how possibly imperfect predictions can be used for 3D object reconstruction.