pwc-archive/papers-with-abstracts huggingface dataset:
Fine-grained linguistic evaluation for state-of-the-art Machine Translation
Authors: Eleftherios Avramidis, Vivien Macketanz, Ursula Strohriegel, Aljoscha Burchardt, Sebastian Möller
Date: 2020-10-13 00:00:00
Proceeding: WMT (EMNLP) 2020 11
Tasks: Machine Translation, Translation

This paper describes a test suite submission providing detailed statistics of linguistic performance for the state-of-the-art German-English systems of the Fifth Conference of Machine Translation (WMT20). The analysis covers 107 phenomena organized in 14 categories based on about 5,500 test items, including a manual annotation effort of 45 person hours. Two systems (Tohoku and Huoshan) appear to have significantly better test suite accuracy than the others, although the best system of WMT20 is not significantly better than the one from WMT19 in a macro-average. Additionally, we identify some linguistic phenomena where all systems suffer (such as idioms, resultative predicates and pluperfect), but we are also able to identify particular weaknesses for individual systems (such as quotation marks, lexical ambiguity and sluicing). Most of the systems of WMT19 which submitted new versions this year show improvements.
CAPT: Contrastive Pre-Training for Learning Denoised Sequence Representations
Authors: Fuli Luo, Pengcheng Yang, Shicheng Li, Xuancheng Ren, Xu sun
Date: 2020-10-13 00:00:00
Tasks: Natural Language Understanding, Sentence

Pre-trained self-supervised models such as BERT have achieved striking success in learning sequence representations, especially for natural language processing. These models typically corrupt the given sequences with certain types of noise, such as masking, shuffling, or substitution, and then try to recover the original input. However, such pre-training approaches are prone to learning representations that are covariant with the noise, leading to the discrepancy between the pre-training and fine-tuning stage. To remedy this, we present ContrAstive Pre-Training (CAPT) to learn noise invariant sequence representations. The proposed CAPT encourages the consistency between representations of the original sequence and its corrupted version via unsupervised instance-wise training signals. In this way, it not only alleviates the pretrain-finetune discrepancy induced by the noise of pre-training, but also aids the pre-trained model in better capturing global semantics of the input via more effective sentence-level supervision. Different from most prior work that focuses on a particular modality, comprehensive empirical evidence on 11 natural language understanding and cross-modal tasks illustrates that CAPT is applicable for both language and vision-language tasks, and obtains surprisingly consistent improvement, including 0.6\% absolute gain on GLUE benchmarks and 0.8\% absolute increment on $\text{NLVR}^2$.
Collaborative Video Object Segmentation by Multi-Scale Foreground-Background Integration
Authors: Zongxin Yang, Yunchao Wei, Yi Yang
Date: 2020-10-13 00:00:00
Tasks: Object, One-shot visual object segmentation, Segmentation, Semantic Segmentation, Semi-Supervised Video Object Segmentation, Video Object Segmentation, Video Semantic Segmentation

This paper investigates the principles of embedding learning to tackle the challenging semi-supervised video object segmentation. Unlike previous practices that focus on exploring the embedding learning of foreground object (s), we consider background should be equally treated. Thus, we propose a Collaborative video object segmentation by Foreground-Background Integration (CFBI) approach. CFBI separates the feature embedding into the foreground object region and its corresponding background region, implicitly promoting them to be more contrastive and improving the segmentation results accordingly. Moreover, CFBI performs both pixel-level matching processes and instance-level attention mechanisms between the reference and the predicted sequence, making CFBI robust to various object scales. Based on CFBI, we introduce a multi-scale matching structure and propose an Atrous Matching strategy, resulting in a more robust and efficient framework, CFBI+. We conduct extensive experiments on two popular benchmarks, i.e., DAVIS and YouTube-VOS. Without applying any simulated data for pre-training, our CFBI+ achieves the performance (J&F) of 82.9% and 82.8%, outperforming all the other state-of-the-art methods. Code: https://github.com/z-x-yang/CFBI.
Assessing the Helpfulness of Review Content for Explaining Recommendations
Authors: D. C. Hernandez-Bocanegra, J. Ziegler
Date: 2020-10-13 00:00:00
Tasks: Recommendation Systems

Despite the maturity already achieved by recommender systems algorithms, little is known about how to obtain and provide users with a proper rationale for a recommendation. Transparency and effectiveness of recommender systems may be increased when explanations are provided. In particular, identifying of helpful argumentative content from reviews can be leveraged to generate textual explanations. In this paper, we investigate the reasons why a review might be considered helpful, and show that the perception of credibility and convincingness mediates the relationship between helpfulness and the perception of objectivity and relevant aspects addressed. Our findings led us to suggest an argumentbased approach to automatically extracting helpful content from hotel reviews, a domain that differs from those that best fit classical argumentation theories.
Balancing Constraints and Rewards with Meta-Gradient D4PG
Authors: Dan A. Calian, Daniel J. Mankowitz, Tom Zahavy, Zhongwen Xu, Junhyuk Oh, Nir Levine, Timothy Mann
Date: 2020-10-13 00:00:00
Proceeding: ICLR 2021 1
Tasks: MuJoCo, Reinforcement Learning (RL)

Deploying Reinforcement Learning (RL) agents to solve real-world applications often requires satisfying complex system constraints. Often the constraint thresholds are incorrectly set due to the complex nature of a system or the inability to verify the thresholds offline (e.g, no simulator or reasonable offline evaluation procedure exists). This results in solutions where a task cannot be solved without violating the constraints. However, in many real-world cases, constraint violations are undesirable yet they are not catastrophic, motivating the need for soft-constrained RL approaches. We present a soft-constrained RL approach that utilizes meta-gradients to find a good trade-off between expected return and minimizing constraint violations. We demonstrate the effectiveness of this approach by showing that it consistently outperforms the baselines across four different MuJoCo domains.
LM-Reloc: Levenberg-Marquardt Based Direct Visual Relocalization
Authors: Lukas von Stumberg, Patrick Wenzel, Nan Yang, Daniel Cremers
Date: 2020-10-13 00:00:00
Tasks: Pose Estimation

We present LM-Reloc -- a novel approach for visual relocalization based on direct image alignment. In contrast to prior works that tackle the problem with a feature-based formulation, the proposed method does not rely on feature matching and RANSAC. Hence, the method can utilize not only corners but any region of the image with gradients. In particular, we propose a loss formulation inspired by the classical Levenberg-Marquardt algorithm to train LM-Net. The learned features significantly improve the robustness of direct image alignment, especially for relocalization across different conditions. To further improve the robustness of LM-Net against large image baselines, we propose a pose estimation network, CorrPoseNet, which regresses the relative pose to bootstrap the direct image alignment. Evaluations on the CARLA and Oxford RobotCar relocalization tracking benchmark show that our approach delivers more accurate results than previous state-of-the-art methods while being comparable in terms of robustness.
Audio-Visual Self-Supervised Terrain Type Discovery for Mobile Platforms
Authors: Akiyoshi Kurobe, Yoshikatsu Nakajima, Hideo Saito, Kris Kitani
Date: 2020-10-13 00:00:00
Tasks: Autonomous Vehicles, Self-Supervised Learning, Vocal Bursts Type Prediction

The ability to both recognize and discover terrain characteristics is an important function required for many autonomous ground robots such as social robots, assistive robots, autonomous vehicles, and ground exploration robots. Recognizing and discovering terrain characteristics is challenging because similar terrains may have very different appearances (e.g., carpet comes in many colors), while terrains with very similar appearance may have very different physical properties (e.g. mulch versus dirt). In order to address the inherent ambiguity in vision-based terrain recognition and discovery, we propose a multi-modal self-supervised learning technique that switches between audio features extracted from a mic attached to the underside of a mobile platform and image features extracted by a camera on the platform to cluster terrain types. The terrain cluster labels are then used to train an image-based convolutional neural network to predict changes in terrain types. Through experiments, we demonstrate that the proposed self-supervised terrain type discovery method achieves over 80% accuracy, which greatly outperforms several baselines and suggests strong potential for assistive applications.
Controllable Pareto Multi-Task Learning
Authors: Xi Lin, Zhiyuan Yang, Qingfu Zhang, Sam Kwong
Date: 2020-10-13 00:00:00
Tasks: Multiobjective Optimization, Multi-Task Learning

A multi-task learning (MTL) system aims at solving multiple related tasks at the same time. With a fixed model capacity, the tasks would be conflicted with each other, and the system usually has to make a trade-off among learning all of them together. For many real-world applications where the trade-off has to be made online, multiple models with different preferences over tasks have to be trained and stored. This work proposes a novel controllable Pareto multi-task learning framework, to enable the system to make real-time trade-off control among different tasks with a single model. To be specific, we formulate the MTL as a preference-conditioned multiobjective optimization problem, with a parametric mapping from preferences to the corresponding trade-off solutions. A single hypernetwork-based multi-task neural network is built to learn all tasks with different trade-off preferences among them, where the hypernetwork generates the model parameters conditioned on the preference. For inference, MTL practitioners can easily control the model performance based on different trade-off preferences in real-time. Experiments on different applications demonstrate that the proposed model is efficient for solving various MTL problems.
Cross-Supervised Joint-Event-Extraction with Heterogeneous Information Networks
Authors: Yue Wang, Zhuo Xu, Lu Bai, Yao Wan, Lixin Cui, Qian Zhao, Edwin R. Hancock, Philip S. Yu
Date: 2020-10-13 00:00:00
Tasks: Event Extraction, TAG

Joint-event-extraction, which extracts structural information (i.e., entities or triggers of events) from unstructured real-world corpora, has attracted more and more research attention in natural language processing. Most existing works do not fully address the sparse co-occurrence relationships between entities and triggers, which loses this important information and thus deteriorates the extraction performance. To mitigate this issue, we first define the joint-event-extraction as a sequence-to-sequence labeling task with a tag set composed of tags of triggers and entities. Then, to incorporate the missing information in the aforementioned co-occurrence relationships, we propose a Cross-Supervised Mechanism (CSM) to alternately supervise the extraction of either triggers or entities based on the type distribution of each other. Moreover, since the connected entities and triggers naturally form a heterogeneous information network (HIN), we leverage the latent pattern along meta-paths for a given corpus to further improve the performance of our proposed method. To verify the effectiveness of our proposed method, we conduct extensive experiments on four real-world datasets as well as compare our method with state-of-the-art methods. Empirical results and analysis show that our approach outperforms the state-of-the-art methods in both entity and trigger extraction.
How important are faces for person re-identification?
Authors: Julia Dietlmeier, Joseph Antony, Kevin McGuinness, Noel E. O'Connor
Date: 2020-10-13 00:00:00
Tasks: Computational Efficiency, Face Detection, Person Re-Identification

This paper investigates the dependence of existing state-of-the-art person re-identification models on the presence and visibility of human faces. We apply a face detection and blurring algorithm to create anonymized versions of several popular person re-identification datasets including Market1501, DukeMTMC-reID, CUHK03, Viper, and Airport. Using a cross-section of existing state-of-the-art models that range in accuracy and computational efficiency, we evaluate the effect of this anonymization on re-identification performance using standard metrics. Perhaps surprisingly, the effect on mAP is very small, and accuracy is recovered by simply training on the anonymized versions of the data rather than the original data. These findings are consistent across multiple models and datasets. These results indicate that datasets can be safely anonymized by blurring faces without significantly impacting the performance of person reidentification systems, and may allow for the release of new richer re-identification datasets where previously there were privacy or data protection concerns.
Extending Implicit Discourse Relation Recognition to the PDTB-3
Authors: Li Liang, Zheng Zhao, Bonnie Webber
Date: 2020-10-13 00:00:00
Proceeding: EMNLP (CODI) 2020 11
Tasks: Implicit Relations, Relation

The PDTB-3 contains many more Implicit discourse relations than the previous PDTB-2. This is in part because implicit relations have now been annotated within sentences as well as between them. In addition, some now co-occur with explicit discourse relations, instead of standing on their own. Here we show that while this can complicate the problem of identifying the location of implicit discourse relations, it can in turn simplify the problem of identifying their senses. We present data to support this claim, as well as methods that can serve as a non-trivial baseline for future state-of-the-art recognizers for implicit discourse relations.
Impact of Thermal Throttling on Long-Term Visual Inference in a CPU-based Edge Device
Authors: Théo Benoit-Cattin, Delia Velasco-Montero, Jorge Fernández-Berni
Date: 2020-10-13 00:00:00
Tasks: CPU

Many application scenarios of edge visual inference, e.g., robotics or environmental monitoring, eventually require long periods of continuous operation. In such periods, the processor temperature plays a critical role to keep a prescribed frame rate. Particularly, the heavy computational load of convolutional neural networks (CNNs) may lead to thermal throttling and hence performance degradation in few seconds. In this paper, we report and analyze the long-term performance of 80 different cases resulting from running 5 CNN models on 4 software frameworks and 2 operating systems without and with active cooling. This comprehensive study was conducted on a low-cost edge platform, namely Raspberry Pi 4B (RPi4B), under stable indoor conditions. The results show that hysteresis-based active cooling prevented thermal throttling in all cases, thereby improving the throughput up to approximately 90% versus no cooling. Interestingly, the range of fan usage during active cooling varied from 33% to 65%. Given the impact of the fan on the power consumption of the system as a whole, these results stress the importance of a suitable selection of CNN model and software components. To assess the performance in outdoor applications, we integrated an external temperature sensor with the RPi4B and conducted a set of experiments with no active cooling in a wide interval of ambient temperature, ranging from 22 {\deg}C to 36 {\deg}C. Variations up to 27.7% were measured with respect to the maximum throughput achieved in that interval. This demonstrates that ambient temperature is a critical parameter in case active cooling cannot be applied.
Lightweight IoT Malware Detection Solution Using CNN Classification
Authors: Ahmad M. N. Zaza, Suleiman K. Kharroub, Khalid Abualsaud
Date: 2020-10-13 00:00:00
Tasks: Classification, General Classification, Malware Detection

Internet of Things (IoT) is becoming more frequently used in more applications as the number of connected devices is in a rapid increase. More connected devices result in bigger challenges in terms of scalability, maintainability and most importantly security especially when it comes to 5G networks. The security aspect of IoT devices is an infant field, which is why it is our focus in this paper. Multiple IoT device manufacturers do not consider securing the devices they produce for different reasons like cost reduction or to avoid using energy-harvesting components. Such potentially malicious devices might be exploited by the adversary to do multiple harmful attacks. Therefore, we developed a system that can recognize malicious behavior of a specific IoT node on the network. Through convolutional neural network and monitoring, we were able to provide malware detection for IoT using a central node that can be installed within the network. The achievement shows how such models can be generalized and applied easily to any network while clearing out any stigma regarding deep learning techniques.
Land Cover Semantic Segmentation Using ResUNet
Authors: Vasilis Pollatos, Loukas Kouvaras, Eleni Charou
Date: 2020-10-13 00:00:00
Tasks: Data Augmentation, Land Cover Classification, Segmentation, Semantic Segmentation, Transfer Learning

In this paper we present our work on developing an automated system for land cover classification. This system takes a multiband satellite image of an area as input and outputs the land cover map of the area at the same resolution as the input. For this purpose convolutional machine learning models were trained in the task of predicting the land cover semantic segmentation of satellite images. This is a case of supervised learning. The land cover label data were taken from the CORINE Land Cover inventory and the satellite images were taken from the Copernicus hub. As for the model, U-Net architecture variations were applied. Our area of interest are the Ionian islands (Greece). We created a dataset from scratch covering this particular area. In addition, transfer learning from the BigEarthNet dataset [1] was performed. In [1] simple classification of satellite images into the classes of CLC is performed but not segmentation as we do. However, their models have been trained into a dataset much bigger than ours, so we applied transfer learning using their pretrained models as the first part of out network, utilizing the ability these networks have developed to extract useful features from the satellite images (we transferred a pretrained ResNet50 into a U-Res-Net). Apart from transfer learning other techniques were applied in order to overcome the limitations set by the small size of our area of interest. We used data augmentation (cutting images into overlapping patches, applying random transformations such as rotations and flips) and cross validation. The results are tested on the 3 CLC class hierarchy levels and a comparative study is made on the results of different approaches.
F1 is Not Enough! Models and Evaluation Towards User-Centered Explainable Question Answering
Authors: Hendrik Schuff, Heike Adel, Ngoc Thang Vu
Date: 2020-10-13 00:00:00
Proceeding: EMNLP 2020 11
Tasks: Model Selection, Question Answering

Explainable question answering systems predict an answer together with an explanation showing why the answer has been selected. The goal is to enable users to assess the correctness of the system and understand its reasoning process. However, we show that current models and evaluation settings have shortcomings regarding the coupling of answer and explanation which might cause serious issues in user experience. As a remedy, we propose a hierarchical model and a new regularization term to strengthen the answer-explanation coupling as well as two evaluation scores to quantify the coupling. We conduct experiments on the HOTPOTQA benchmark data set and perform a user study. The user study shows that our models increase the ability of the users to judge the correctness of the system and that scores like F1 are not enough to estimate the usefulness of a model in a practical setting with human users. Our scores are better aligned with user experience, making them promising candidates for model selection.
RGCL at SemEval-2020 Task 6: Neural Approaches to Definition Extraction
Authors: Tharindu Ranasinghe, Alistair Plum, Constantin Orasan, Ruslan Mitkov
Date: 2020-10-13 00:00:00
Tasks: Definition Extraction, Sentence

This paper presents the RGCL team submission to SemEval 2020 Task 6: DeftEval, subtasks 1 and 2. The system classifies definitions at the sentence and token levels. It utilises state-of-the-art neural network architectures, which have some task-specific adaptations, including an automatically extended training set. Overall, the approach achieves acceptable evaluation scores, while maintaining flexibility in architecture selection.
BRUMS at SemEval-2020 Task 12 : Transformer based Multilingual Offensive Language Identification in Social Media
Authors: Tharindu Ranasinghe, Hansi Hettiarachchi
Date: 2020-10-13 00:00:00
Tasks: Language Identification

In this paper, we describe the team \textit{BRUMS} entry to OffensEval 2: Multilingual Offensive Language Identification in Social Media in SemEval-2020. The OffensEval organizers provided participants with annotated datasets containing posts from social media in Arabic, Danish, English, Greek and Turkish. We present a multilingual deep learning model to identify offensive language in social media. Overall, the approach achieves acceptable evaluation scores, while maintaining flexibility between languages.
BRUMS at SemEval-2020 Task 3: Contextualised Embeddings for Predicting the (Graded) Effect of Context in Word Similarity
Authors: Hansi Hettiarachchi, Tharindu Ranasinghe
Date: 2020-10-13 00:00:00
Proceeding: SEMEVAL 2020
Tasks: Position, Word Embeddings, Word Similarity

This paper presents the team BRUMS submission to SemEval-2020 Task 3: Graded Word Similarity in Context. The system utilises state-of-the-art contextualised word embeddings, which have some task-specific adaptations, including stacked embeddings and average embeddings. Overall, the approach achieves good evaluation scores across all the languages, while maintaining simplicity. Following the final rankings, our approach is ranked within the top 5 solutions of each language while preserving the 1st position of Finnish subtask 2.