pwc-archive/papers-with-abstracts huggingface dataset:
Watch, read and lookup: learning to spot signs from multiple supervisors
Authors: Liliane Momeni, Gül Varol, Samuel Albanie, Triantafyllos Afouras, Andrew Zisserman
Date: 2020-10-08 00:00:00
Tasks: Multiple Instance Learning

The focus of this work is sign spotting - given a video of an isolated sign, our task is to identify whether and where it has been signed in a continuous, co-articulated sign language video. To achieve this sign spotting task, we train a model using multiple types of available supervision by: (1) watching existing sparsely labelled footage; (2) reading associated subtitles (readily available translations of the signed content) which provide additional weak-supervision; (3) looking up words (for which no co-articulated labelled examples are available) in visual sign language dictionaries to enable novel sign spotting. These three tasks are integrated into a unified learning framework using the principles of Noise Contrastive Estimation and Multiple Instance Learning. We validate the effectiveness of our approach on low-shot sign spotting benchmarks. In addition, we contribute a machine-readable British Sign Language (BSL) dictionary dataset of isolated signs, BSLDict, to facilitate study of this task. The dataset, models and code are available at our project page.
GRADE: Automatic Graph-Enhanced Coherence Metric for Evaluating Open-Domain Dialogue Systems
Authors: Lishan Huang, Zheng Ye, Jinghui Qin, Liang Lin, Xiaodan Liang
Date: 2020-10-08 00:00:00
Proceeding: EMNLP 2020 11
Tasks: Dialogue Evaluation

Automatically evaluating dialogue coherence is a challenging but high-demand ability for developing high-quality open-domain dialogue systems. However, current evaluation metrics consider only surface features or utterance-level semantics, without explicitly considering the fine-grained topic transition dynamics of dialogue flows. Here, we first consider that the graph structure constituted with topics in a dialogue can accurately depict the underlying communication logic, which is a more natural way to produce persuasive metrics. Capitalized on the topic-level dialogue graph, we propose a new evaluation metric GRADE, which stands for Graph-enhanced Representations for Automatic Dialogue Evaluation. Specifically, GRADE incorporates both coarse-grained utterance-level contextualized representations and fine-grained topic-level graph representations to evaluate dialogue coherence. The graph representations are obtained by reasoning over topic-level dialogue graphs enhanced with the evidence from a commonsense graph, including k-hop neighboring representations and hop-attention weights. Experimental results show that our GRADE significantly outperforms other state-of-the-art metrics on measuring diverse dialogue models in terms of the Pearson and Spearman correlations with human judgements. Besides, we release a new large-scale human evaluation benchmark to facilitate future research on automatic metrics.
UESegNet: Context Aware Unconstrained ROI Segmentation Networks for Ear Biometric
Authors: Aman Kamboj, Rajneesh Rani, Aditya Nigam, Ranjeet Ranjan Jha
Date: 2020-10-08 00:00:00
Tasks: object-detection, Object Detection, Region Proposal

Biometric-based personal authentication systems have seen a strong demand mainly due to the increasing concern in various privacy and security applications. Although the use of each biometric trait is problem dependent, the human ear has been found to have enough discriminating characteristics to allow its use as a strong biometric measure. To locate an ear in a 2D side face image is a challenging task, numerous existing approaches have achieved significant performance, but the majority of studies are based on the constrained environment. However, ear biometrics possess a great level of difficulties in the unconstrained environment, where pose, scale, occlusion, illuminations, background clutter etc. varies to a great extent. To address the problem of ear localization in the wild, we have proposed two high-performance region of interest (ROI) segmentation models UESegNet-1 and UESegNet-2, which are fundamentally based on deep convolutional neural networks and primarily uses contextual information to localize ear in the unconstrained environment. Additionally, we have applied state-of-the-art deep learning models viz; FRCNN (Faster Region Proposal Network) and SSD (Single Shot MultiBox Detecor) for ear localization task. To test the model's generalization, they are evaluated on six different benchmark datasets viz; IITD, IITK, USTB-DB3, UND-E, UND-J2 and UBEAR, all of which contain challenging images. The performance of the models is compared on the basis of object detection performance measure parameters such as IOU (Intersection Over Union), Accuracy, Precision, Recall, and F1-Score. It has been observed that the proposed models UESegNet-1 and UESegNet-2 outperformed the FRCNN and SSD at higher values of IOUs i.e. an accuracy of 100\% is achieved at IOU 0.5 on majority of the databases.
Free annotated data for deep learning in microscopy? A hitchhiker's guide
Authors: Adrian Shajkofci, Michael Liebling
Date: 2020-10-08 00:00:00

In microscopy, the time burden and cost of acquiring and annotating large datasets that many deep learning models take as a prerequisite, often appears to make these methods impractical. Can this requirement for annotated data be relaxed? Is it possible to borrow the knowledge gathered from datasets in other application fields and leverage it for microscopy? Here, we aim to provide an overview of methods that have recently emerged to successfully train learning-based methods in bio-microscopy.
Generating Instructions at Different Levels of Abstraction
Authors: Arne Köhn, Julia Wichlacz, Álvaro Torralba, Daniel Höller, Jörg Hoffmann, Alexander Koller
Date: 2020-10-08 00:00:00
Proceeding: COLING 2020 8
Tasks: Minecraft, Object

When generating technical instructions, it is often convenient to describe complex objects in the world at different levels of abstraction. A novice user might need an object explained piece by piece, while for an expert, talking about the complex object (e.g. a wall or railing) directly may be more succinct and efficient. We show how to generate building instructions at different levels of abstraction in Minecraft. We introduce the use of hierarchical planning to this end, a method from AI planning which can capture the structure of complex objects neatly. A crowdsourcing evaluation shows that the choice of abstraction level matters to users, and that an abstraction strategy which balances low-level and high-level object descriptions compares favorably to ones which don't.
A Variational Auto-Encoder Approach for Image Transmission in Wireless Channel
Authors: Amir Hossein Estiri, Mohammad Reza Sabramooz, Ali Banaei, Amir Hossein Dehghan, Benyamin Jamialahmadi, Mahdi Jafari Siavoshani
Date: 2020-10-08 00:00:00
Tasks: SSIM

Recent advancements in information technology and the widespread use of the Internet have led to easier access to data worldwide. As a result, transmitting data through noisy channels is inevitable. Reducing the size of data and protecting it during transmission from corruption due to channel noises are two classical problems in communication and information theory. Recently, inspired by deep neural networks' success in different tasks, many works have been done to address these two problems using deep learning techniques. In this paper, we investigate the performance of variational auto-encoders and compare the results with standard auto-encoders. Our findings suggest that variational auto-encoders are more robust to channel degradation than auto-encoders. Furthermore, we have tried to excel in the human perceptual quality of reconstructed images by using perception-based error metrics as our network's loss function. To this end, we use the structural similarity index (SSIM) as a perception-based metric to optimize the proposed neural network. Our experiments demonstrate that the SSIM metric visually improves the quality of the reconstructed images at the receiver.
Prioritized Level Replay
Authors: Minqi Jiang, Edward Grefenstette, Tim Rocktäschel
Date: 2020-10-08 00:00:00
Tasks: Deep Reinforcement Learning, Systematic Generalization

Environments with procedurally generated content serve as important benchmarks for testing systematic generalization in deep reinforcement learning. In this setting, each level is an algorithmically created environment instance with a unique configuration of its factors of variation. Training on a prespecified subset of levels allows for testing generalization to unseen levels. What can be learned from a level depends on the current policy, yet prior work defaults to uniform sampling of training levels independently of the policy. We introduce Prioritized Level Replay (PLR), a general framework for selectively sampling the next training level by prioritizing those with higher estimated learning potential when revisited in the future. We show TD-errors effectively estimate a level's future learning potential and, when used to guide the sampling procedure, induce an emergent curriculum of increasingly difficult levels. By adapting the sampling of training levels, PLR significantly improves sample efficiency and generalization on Procgen Benchmark--matching the previous state-of-the-art in test return--and readily combines with other methods. Combined with the previous leading method, PLR raises the state-of-the-art to over 76% improvement in test return relative to standard RL baselines.
Assessing Classifier Fairness with Collider Bias
Authors: Zhenlong Xu, Jixue Liu, Debo Cheng, Jiuyong Li, Lin Liu, Ke Wang, Ziqi Xu, Zhenlong Xu contributed equally to this paper
Date: 2020-10-08 00:00:00
Tasks: Decision Making, Fairness

The increasing application of machine learning techniques in everyday decision-making processes has brought concerns about the fairness of algorithmic decision-making. This paper concerns the problem of collider bias which produces spurious associations in fairness assessment and develops theorems to guide fairness assessment avoiding the collider bias. We consider a real-world application of auditing a trained classifier by an audit agency. We propose an unbiased assessment algorithm by utilising the developed theorems to reduce collider biases in the assessment. Experiments and simulations show the proposed algorithm reduces collider biases significantly in the assessment and is promising in auditing trained classifiers.
Predicting Typological Features in WALS using Language Embeddings and Conditional Probabilities: ÚFAL Submission to the SIGTYP 2020 Shared Task
Authors: Martin Vastl, Daniel Zeman, Rudolf Rosa
Date: 2020-10-08 00:00:00
Proceeding: EMNLP (SIGTYP) 2020 11

We present our submission to the SIGTYP 2020 Shared Task on the prediction of typological features. We submit a constrained system, predicting typological features only based on the WALS database. We investigate two approaches. The simpler of the two is a system based on estimating correlation of feature values within languages by computing conditional probabilities and mutual information. The second approach is to train a neural predictor operating on precomputed language embeddings based on WALS features. Our submitted system combines the two approaches based on their self-estimated confidence scores. We reach the accuracy of 70.7% on the test data and rank first in the shared task.
Near-Optimal Comparison Based Clustering
Authors: Michaël Perrot, Pascal Mattia Esser, Debarghya Ghoshdastidar
Date: 2020-10-08 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: Clustering

The goal of clustering is to group similar objects into meaningful partitions. This process is well understood when an explicit similarity measure between the objects is given. However, far less is known when this information is not readily available and, instead, one only observes ordinal comparisons such as "object i is more similar to j than to k." In this paper, we tackle this problem using a two-step procedure: we estimate a pairwise similarity matrix from the comparisons before using a clustering method based on semi-definite programming (SDP). We theoretically show that our approach can exactly recover a planted clustering using a near-optimal number of passive comparisons. We empirically validate our theoretical findings and demonstrate the good behaviour of our method on real data.
AT-MFCGA: An Adaptive Transfer-guided Multifactorial Cellular Genetic Algorithm for Evolutionary Multitasking
Authors: Eneko Osaba, Javier Del Ser, Aritz D. Martinez, Jesus L. Lobo, Francisco Herrera
Date: 2020-10-08 00:00:00
Tasks: Combinatorial Optimization

Transfer Optimization is an incipient research area dedicated to solving multiple optimization tasks simultaneously. Among the different approaches that can address this problem effectively, Evolutionary Multitasking resorts to concepts from Evolutionary Computation to solve multiple problems within a single search process. In this paper we introduce a novel adaptive metaheuristic algorithm to deal with Evolutionary Multitasking environments coined as Adaptive Transfer-guided Multifactorial Cellular Genetic Algorithm (AT-MFCGA). AT-MFCGA relies on cellular automata to implement mechanisms in order to exchange knowledge among the optimization problems under consideration. Furthermore, our approach is able to explain by itself the synergies among tasks that were encountered and exploited during the search, which helps us to understand interactions between related optimization tasks. A comprehensive experimental setup is designed to assess and compare the performance of AT-MFCGA to that of other renowned evolutionary multitasking alternatives (MFEA and MFEA-II). Experiments comprise 11 multitasking scenarios composed of 20 instances of 4 combinatorial optimization problems, yielding the largest discrete multitasking environment solved to date. Results are conclusive in regard to the superior quality of solutions provided by AT-MFCGA with respect to the rest of the methods, which are complemented by a quantitative examination of the genetic transferability among tasks throughout the search process.
Injecting Word Information with Multi-Level Word Adapter for Chinese Spoken Language Understanding
Authors: Dechuan Teng, Libo Qin, Wanxiang Che, Sendong Zhao, Ting Liu
Date: 2020-10-08 00:00:00
Tasks: Intent Detection, Sentence, slot-filling, Slot Filling, Spoken Language Understanding

In this paper, we improve Chinese spoken language understanding (SLU) by injecting word information. Previous studies on Chinese SLU do not consider the word information, failing to detect word boundaries that are beneficial for intent detection and slot filling. To address this issue, we propose a multi-level word adapter to inject word information for Chinese SLU, which consists of (1) sentence-level word adapter, which directly fuses the sentence representations of the word information and character information to perform intent detection and (2) character-level word adapter, which is applied at each character for selectively controlling weights on word information as well as character information. Experimental results on two Chinese SLU datasets show that our model can capture useful word information and achieve state-of-the-art performance.
IRX-1D: A Simple Deep Learning Architecture for Remote Sensing Classifications
Authors: Mahesh Pal, Akshay, B. Charan Teja
Date: 2020-10-08 00:00:00
Tasks: Classification, Deep Learning, General Classification

We proposes a simple deep learning architecture combining elements of Inception, ResNet and Xception networks. Four new datasets were used for classification with both small and large training samples. Results in terms of classification accuracy suggests improved performance by proposed architecture in comparison to Bayesian optimised 2D-CNN with small training samples. Comparison of results using small training sample with Indiana Pines hyperspectral dataset suggests comparable or better performance by proposed architecture than nine reported works using different deep learning architectures. In spite of achieving high classification accuracy with limited training samples, comparison of classified image suggests different land cover classes are assigned to same area when compared with the classified image provided by the model trained using large training samples with all datasets.
Population Based Training for Data Augmentation and Regularization in Speech Recognition
Authors: Daniel Haziza, Jérémy Rapin, Gabriel Synnaeve
Date: 2020-10-08 00:00:00
Tasks: Data Augmentation, speech-recognition, Speech Recognition

Varying data augmentation policies and regularization over the course of optimization has led to performance improvements over using fixed values. We show that population based training is a useful tool to continuously search those hyperparameters, within a fixed budget. This greatly simplifies the experimental burden and computational cost of finding such optimal schedules. We experiment in speech recognition by optimizing SpecAugment this way, as well as dropout. It compares favorably to a baseline that does not change those hyperparameters over the course of training, with an 8% relative WER improvement. We obtain 5.18% word error rate on LibriSpeech's test-other.
BGM: Building a Dynamic Guidance Map without Visual Images for Trajectory Prediction
Authors: Beihao Xia, Conghao Wong, Heng Li, Shiming Chen, Qinmu Peng, Xinge You
Date: 2020-10-08 00:00:00
Tasks: Decoder, Trajectory Prediction

Visual images usually contain the informative context of the environment, thereby helping to predict agents' behaviors. However, they hardly impose the dynamic effects on agents' actual behaviors due to the respectively fixed semantics. To solve this problem, we propose a deterministic model named BGM to construct a guidance map to represent the dynamic semantics, which circumvents to use visual images for each agent to reflect the difference of activities in different periods. We first record all agents' activities in the scene within a period close to the current to construct a guidance map and then feed it to a Context CNN to obtain their context features. We adopt a Historical Trajectory Encoder to extract the trajectory features and then combine them with the context feature as the input of the social energy based trajectory decoder, thus obtaining the prediction that meets the social rules. Experiments demonstrate that BGM achieves state-of-the-art prediction accuracy on the two widely used ETH and UCY datasets and handles more complex scenarios.
Large Product Key Memory for Pretrained Language Models
Authors: Gyuwan Kim, Tae-Hwan Jung
Date: 2020-10-08 00:00:00
Proceeding: Findings of the Association for Computational Linguistics 2020
Tasks: Causal Language Modeling, Language Modeling, Language Modelling

Product key memory (PKM) proposed by Lample et al. (2019) enables to improve prediction accuracy by increasing model capacity efficiently with insignificant computational overhead. However, their empirical application is only limited to causal language modeling. Motivated by the recent success of pretrained language models (PLMs), we investigate how to incorporate large PKM into PLMs that can be finetuned for a wide variety of downstream NLP tasks. We define a new memory usage metric, and careful observation using this metric reveals that most memory slots remain outdated during the training of PKM-augmented models. To train better PLMs by tackling this issue, we propose simple but effective solutions: (1) initialization from the model weights pretrained without memory and (2) augmenting PKM by addition rather than replacing a feed-forward network. We verify that both of them are crucial for the pretraining of PKM-augmented PLMs, enhancing memory utilization and downstream performance. Code and pretrained weights are available at https://github.com/clovaai/pkm-transformers.
A Co-Interactive Transformer for Joint Slot Filling and Intent Detection
Authors: Libo Qin, Tailu Liu, Wanxiang Che, Bingbing Kang, Sendong Zhao, Ting Liu
Date: 2020-10-08 00:00:00
Tasks: Intent Detection, slot-filling, Slot Filling, Spoken Language Understanding

Intent detection and slot filling are two main tasks for building a spoken language understanding (SLU) system. The two tasks are closely related and the information of one task can be utilized in the other task. Previous studies either model the two tasks separately or only consider the single information flow from intent to slot. None of the prior approaches model the bidirectional connection between the two tasks simultaneously. In this paper, we propose a Co-Interactive Transformer to consider the cross-impact between the two tasks. Instead of adopting the self-attention mechanism in vanilla Transformer, we propose a co-interactive module to consider the cross-impact by building a bidirectional connection between the two related tasks. In addition, the proposed co-interactive module can be stacked to incrementally enhance each other with mutual features. The experimental results on two public datasets (SNIPS and ATIS) show that our model achieves the state-of-the-art performance with considerable improvements (+3.4% and +0.9% on overall acc). Extensive experiments empirically verify that our model successfully captures the mutual interaction knowledge.
Clinically Verified Hybrid Deep Learning System for Retinal Ganglion Cells Aware Grading of Glaucomatous Progression
Authors: Hina Raja, Taimur Hassan, Muhammad Usman Akram, Naoufel Werghi
Date: 2020-10-08 00:00:00

Objective: Glaucoma is the second leading cause of blindness worldwide. Glaucomatous progression can be easily monitored by analyzing the degeneration of retinal ganglion cells (RGCs). Many researchers have screened glaucoma by measuring cup-to-disc ratios from fundus and optical coherence tomography scans. However, this paper presents a novel strategy that pays attention to the RGC atrophy for screening glaucomatous pathologies and grading their severity. Methods: The proposed framework encompasses a hybrid convolutional network that extracts the retinal nerve fiber layer, ganglion cell with the inner plexiform layer and ganglion cell complex regions, allowing thus a quantitative screening of glaucomatous subjects. Furthermore, the severity of glaucoma in screened cases is objectively graded by analyzing the thickness of these regions. Results: The proposed framework is rigorously tested on publicly available Armed Forces Institute of Ophthalmology (AFIO) dataset, where it achieved the F1 score of 0.9577 for diagnosing glaucoma, a mean dice coefficient score of 0.8697 for extracting the RGC regions and an accuracy of 0.9117 for grading glaucomatous progression. Furthermore, the performance of the proposed framework is clinically verified with the markings of four expert ophthalmologists, achieving a statistically significant Pearson correlation coefficient of 0.9236. Conclusion: An automated assessment of RGC degeneration yields better glaucomatous screening and grading as compared to the state-of-the-art solutions. Significance: An RGC-aware system not only screens glaucoma but can also grade its severity and here we present an end-to-end solution that is thoroughly evaluated on a standardized dataset and is clinically validated for analyzing glaucomatous pathologies.
What Can We Do to Improve Peer Review in NLP?
Authors: Anna Rogers, Isabelle Augenstein
Date: 2020-10-08 00:00:00
Proceeding: Findings of the Association for Computational Linguistics 2020

Peer review is our best tool for judging the quality of conference submissions, but it is becoming increasingly spurious. We argue that a part of the problem is that the reviewers and area chairs face a poorly defined task forcing apples-to-oranges comparisons. There are several potential ways forward, but the key difficulty is creating the incentives and mechanisms for their consistent implementation in the NLP community.