pwc-archive/papers-with-abstracts huggingface dataset:
ZEST: Zero-shot Learning from Text Descriptions using Textual Similarity and Visual Summarization
Authors: Tzuf Paz-Argaman, Yuval Atzmon, Gal Chechik, Reut Tsarfaty
Date: 2020-10-07 00:00:00
Proceeding: Findings of the Association for Computational Linguistics 2020
Tasks: Zero-Shot Learning

We study the problem of recognizing visual entities from the textual descriptions of their classes. Specifically, given birds' images with free-text descriptions of their species, we learn to classify images of previously-unseen species based on specie descriptions. This setup has been studied in the vision community under the name zero-shot learning from text, focusing on learning to transfer knowledge about visual aspects of birds from seen classes to previously-unseen ones. Here, we suggest focusing on the textual description and distilling from the description the most relevant information to effectively match visual features to the parts of the text that discuss them. Specifically, (1) we propose to leverage the similarity between species, reflected in the similarity between text descriptions of the species. (2) we derive visual summaries of the texts, i.e., extractive summaries that focus on the visual features that tend to be reflected in images. We propose a simple attention-based model augmented with the similarity and visual summaries components. Our empirical results consistently and significantly outperform the state-of-the-art on the largest benchmarks for text-based zero-shot learning, illustrating the critical importance of texts for zero-shot image-recognition.
Learning to Explain: Datasets and Models for Identifying Valid Reasoning Chains in Multihop Question-Answering
Authors: Harsh Jhamtani, Peter Clark
Date: 2020-10-07 00:00:00
Proceeding: EMNLP 2020 11
Tasks: Question Answering, Reasoning Chain Explanations, Retrieval, valid

Despite the rapid progress in multihop question-answering (QA), models still have trouble explaining why an answer is correct, with limited explanation training data available to learn from. To address this, we introduce three explanation datasets in which explanations formed from corpus facts are annotated. Our first dataset, eQASC, contains over 98K explanation annotations for the multihop question answering dataset QASC, and is the first that annotates multiple candidate explanations for each answer. The second dataset eQASC-perturbed is constructed by crowd-sourcing perturbations (while preserving their validity) of a subset of explanations in QASC, to test consistency and generalization of explanation prediction models. The third dataset eOBQA is constructed by adding explanation annotations to the OBQA dataset to test generalization of models trained on eQASC. We show that this data can be used to significantly improve explanation quality (+14% absolute F1 over a strong retrieval baseline) using a BERT-based classifier, but still behind the upper bound, offering a new challenge for future research. We also explore a delexicalized chain representation in which repeated noun phrases are replaced by variables, thus turning them into generalized reasoning chains (for example: "X is a Y" AND "Y has Z" IMPLIES "X has Z"). We find that generalized chains maintain performance while also being more robust to certain perturbations.
Narrative Text Generation with a Latent Discrete Plan
Authors: Harsh Jhamtani, Taylor Berg-Kirkpatrick
Date: 2020-10-07 00:00:00
Proceeding: Findings of the Association for Computational Linguistics 2020
Tasks: Decoder, Sentence, Story Generation, TAG, Text Generation, Variational Inference

Past work on story generation has demonstrated the usefulness of conditioning on a generation plan to generate coherent stories. However, these approaches have used heuristics or off-the-shelf models to first tag training stories with the desired type of plan, and then train generation models in a supervised fashion. In this paper, we propose a deep latent variable model that first samples a sequence of anchor words, one per sentence in the story, as part of its generative process. During training, our model treats the sequence of anchor words as a latent variable and attempts to induce anchoring sequences that help guide generation in an unsupervised fashion. We conduct experiments with several types of sentence decoder distributions: left-to-right and non-monotonic, with different degrees of restriction. Further, since we use amortized variational inference to train our model, we introduce two corresponding types of inference network for predicting the posterior on anchor words. We conduct human evaluations which demonstrate that the stories produced by our model are rated better in comparison with baselines which do not consider story plans, and are similar or better in quality relative to baselines which use external supervision for plans. Additionally, the proposed model gets favorable scores when evaluated on perplexity, diversity, and control of story via discrete plan.
Attention Model Enhanced Network for Classification of Breast Cancer Image
Authors: Xiao Kang, Xingbo Liu, Xiushan Nie, Xiaoming Xi, Yilong Yin
Date: 2020-10-07 00:00:00
Tasks: Cancer Classification, Classification, General Classification

Breast cancer classification remains a challenging task due to inter-class ambiguity and intra-class variability. Existing deep learning-based methods try to confront this challenge by utilizing complex nonlinear projections. However, these methods typically extract global features from entire images, neglecting the fact that the subtle detail information can be crucial in extracting discriminative features. In this study, we propose a novel method named Attention Model Enhanced Network (AMEN), which is formulated in a multi-branch fashion with pixel-wised attention model and classification submodular. Specifically, the feature learning part in AMEN can generate pixel-wised attention map, while the classification submodular are utilized to classify the samples. To focus more on subtle detail information, the sample image is enhanced by the pixel-wised attention map generated from former branch. Furthermore, boosting strategy are adopted to fuse classification results from different branches for better performance. Experiments conducted on three benchmark datasets demonstrate the superiority of the proposed method under various scenarios.
Low-Rank Robust Online Distance/Similarity Learning based on the Rescaled Hinge Loss
Authors: Davood Zabihzadeh, Amar Tuama, Ali Karami-Mollaee
Date: 2020-10-07 00:00:00
Tasks: Metric Learning, Triplet

An important challenge in metric learning is scalability to both size and dimension of input data. Online metric learning algorithms are proposed to address this challenge. Existing methods are commonly based on (Passive Aggressive) PA approach. Hence, they can rapidly process large volumes of data with an adaptive learning rate. However, these algorithms are based on the Hinge loss and so are not robust against outliers and label noise. Also, existing online methods usually assume training triplets or pairwise constraints are exist in advance. However, many datasets in real-world applications are in the form of input data and their associated labels. We address these challenges by formulating the online Distance-Similarity learning problem with the robust Rescaled hinge loss function. The proposed model is rather general and can be applied to any PA-based online Distance-Similarity algorithm. Also, we develop an efficient robust one-pass triplet construction algorithm. Finally, to provide scalability in high dimensional DML environments, the low-rank version of the proposed methods is presented that not only reduces the computational cost significantly but also keeps the predictive performance of the learned metrics. Also, it provides a straightforward extension of our methods for deep Distance-Similarity learning. We conduct several experiments on datasets from various applications. The results confirm that the proposed methods significantly outperform state-of-the-art online DML methods in the presence of label noise and outliers by a large margin.
Learning Binary Semantic Embedding for Histology Image Classification and Retrieval
Authors: Xiao Kang, Xingbo Liu, Xiushan Nie, Yilong Yin
Date: 2020-10-07 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, Retrieval

With the development of medical imaging technology and machine learning, computer-assisted diagnosis which can provide impressive reference to pathologists, attracts extensive research interests. The exponential growth of medical images and uninterpretability of traditional classification models have hindered the applications of computer-assisted diagnosis. To address these issues, we propose a novel method for Learning Binary Semantic Embedding (LBSE). Based on the efficient and effective embedding, classification and retrieval are performed to provide interpretable computer-assisted diagnosis for histology images. Furthermore, double supervision, bit uncorrelation and balance constraint, asymmetric strategy and discrete optimization are seamlessly integrated in the proposed method for learning binary embedding. Experiments conducted on three benchmark datasets validate the superiority of LBSE under various scenarios.
A Novel Face-tracking Mouth Controller and its Application to Interacting with Bioacoustic Models
Authors: Gamhewage C. de Silva, Tamara Smyth, Michael J. Lyons
Date: 2020-10-07 00:00:00
Tasks: Audio Synthesis

We describe a simple, computationally light, real-time system for tracking the lower face and extracting information about the shape of the open mouth from a video sequence. The system allows unencumbered control of audio synthesis modules by the action of the mouth. We report work in progress to use the mouth controller to interact with a physical model of sound production by the avian syrinx.
Improving the Efficiency of Grammatical Error Correction with Erroneous Span Detection and Correction
Authors: Mengyun Chen, Tao Ge, Xingxing Zhang, Furu Wei, Ming Zhou
Date: 2020-10-07 00:00:00
Proceeding: EMNLP 2020 11
Tasks: Grammatical Error Correction, Sentence

We propose a novel language-independent approach to improve the efficiency for Grammatical Error Correction (GEC) by dividing the task into two subtasks: Erroneous Span Detection (ESD) and Erroneous Span Correction (ESC). ESD identifies grammatically incorrect text spans with an efficient sequence tagging model. Then, ESC leverages a seq2seq model to take the sentence with annotated erroneous spans as input and only outputs the corrected text for these spans. Experiments show our approach performs comparably to conventional seq2seq approaches in both English and Chinese GEC benchmarks with less than 50% time cost for inference.
Global Optimization of Objective Functions Represented by ReLU Networks
Authors: Christopher A. Strong, Haoze Wu, Aleksandar Zeljić, Kyle D. Julian, Guy Katz, Clark Barrett, Mykel J. Kochenderfer
Date: 2020-10-07 00:00:00
Tasks: global-optimization

Neural networks can learn complex, non-convex functions, and it is challenging to guarantee their correct behavior in safety-critical contexts. Many approaches exist to find failures in networks (e.g., adversarial examples), but these cannot guarantee the absence of failures. Verification algorithms address this need and provide formal guarantees about a neural network by answering "yes or no" questions. For example, they can answer whether a violation exists within certain bounds. However, individual "yes or no" questions cannot answer qualitative questions such as "what is the largest error within these bounds"; the answers to these lie in the domain of optimization. Therefore, we propose strategies to extend existing verifiers to perform optimization and find: (i) the most extreme failure in a given input region and (ii) the minimum input perturbation required to cause a failure. A naive approach using a bisection search with an off-the-shelf verifier results in many expensive and overlapping calls to the verifier. Instead, we propose an approach that tightly integrates the optimization process into the verification procedure, achieving better runtime performance than the naive approach. We evaluate our approach implemented as an extension of Marabou, a state-of-the-art neural network verifier, and compare its performance with the bisection approach and MIPVerify, an optimization-based verifier. We observe complementary performance between our extension of Marabou and MIPVerify.
Variational Feature Disentangling for Fine-Grained Few-Shot Classification
Authors: Jingyi Xu, Hieu Le, Mingzhen Huang, ShahRukh Athar, Dimitris Samaras
Date: 2020-10-07 00:00:00
Proceeding: ICCV 2021 10
Tasks: Classification, Few-Shot Image Classification, image-classification, Image Classification, Transfer Learning, Variational Inference

Fine-grained few-shot recognition often suffers from the problem of training data scarcity for novel categories.The network tends to overfit and does not generalize well to unseen classes due to insufficient training data. Many methods have been proposed to synthesize additional data to support the training. In this paper, we focus one enlarging the intra-class variance of the unseen class to improve few-shot classification performance. We assume that the distribution of intra-class variance generalizes across the base class and the novel class. Thus, the intra-class variance of the base set can be transferred to the novel set for feature augmentation. Specifically, we first model the distribution of intra-class variance on the base set via variational inference. Then the learned distribution is transferred to the novel set to generate additional features, which are used together with the original ones to train a classifier. Experimental results show a significant boost over the state-of-the-art methods on the challenging fine-grained few-shot image classification benchmarks.
Please Mind the Root: Decoding Arborescences for Dependency Parsing
Authors: Ran Zmigrod, Tim Vieira, Ryan Cotterell
Date: 2020-10-06 00:00:00
Proceeding: EMNLP 2020 11
Tasks: Dependency Parsing

The connection between dependency trees and spanning trees is exploited by the NLP community to train and to decode graph-based dependency parsers. However, the NLP literature has missed an important difference between the two structures: only one edge may emanate from the root in a dependency tree. We analyzed the output of state-of-the-art parsers on many languages from the Universal Dependency Treebank: although these parsers are often able to learn that trees which violate the constraint should be assigned lower probabilities, their ability to do so unsurprisingly de-grades as the size of the training set decreases. In fact, the worst constraint-violation rate we observe is 24%. Prior work has proposed an inefficient algorithm to enforce the constraint, which adds a factor of n to the decoding runtime. We adapt an algorithm due to Gabow and Tarjan (1984) to dependency parsing, which satisfies the constraint without compromising the original runtime.
Reconfigurable Intelligent Surfaces and Machine Learning for Wireless Fingerprinting Localization
Authors: Cam Ly Nguyen, Orestis Georgiou, Gabriele Gradoni
Date: 2020-10-07 00:00:00
Tasks: BIG-bench Machine Learning, Diversity, feature selection, Position

Reconfigurable Intelligent Surfaces (RISs) promise improved, secure and more efficient wireless communications. We propose and demonstrate how to exploit the diversity offered by RISs to generate and select easily differentiable radio maps for use in wireless fingerprinting localization applications. Further, we apply machine learning feature selection methods to prune the large state space of the RIS, thus reducing complexity and enhancing localization accuracy and position acquisition time. We evaluate our proposed approach by generation of radio maps with a novel radio propagation modelling and simulations.
DiffMG: Differentiable Meta Graph Search for Heterogeneous Graph Neural Networks
Authors: Yuhui Ding, Quanming Yao, Huan Zhao, Tong Zhang
Date: 2020-10-07 00:00:00
Tasks: Neural Architecture Search, Recommendation Systems

In this paper, we propose a novel framework to automatically utilize task-dependent semantic information which is encoded in heterogeneous information networks (HINs). Specifically, we search for a meta graph, which can capture more complex semantic relations than a meta path, to determine how graph neural networks (GNNs) propagate messages along different types of edges. We formalize the problem within the framework of neural architecture search (NAS) and then perform the search in a differentiable manner. We design an expressive search space in the form of a directed acyclic graph (DAG) to represent candidate meta graphs for a HIN, and we propose task-dependent type constraint to filter out those edge types along which message passing has no effect on the representations of nodes that are related to the downstream task. The size of the search space we define is huge, so we further propose a novel and efficient search algorithm to make the total search cost on a par with training a single GNN once. Compared with existing popular NAS algorithms, our proposed search algorithm improves the search efficiency. We conduct extensive experiments on different HINs and downstream tasks to evaluate our method, and experimental results show that our method can outperform state-of-the-art heterogeneous GNNs and also improves efficiency compared with those methods which can implicitly learn meta paths.
Exploring and Evaluating Attributes, Values, and Structures for Entity Alignment
Authors: Yixin Cao, Liangming Pan, Juanzi Li, Zhiyuan Liu, Tat-Seng Chua
Date: 2020-10-07 00:00:00
Proceeding: EMNLP 2020 11
Tasks: Attribute, Entity Alignment

Entity alignment (EA) aims at building a unified Knowledge Graph (KG) of rich content by linking the equivalent entities from various KGs. GNN-based EA methods present promising performances by modeling the KG structure defined by relation triples. However, attribute triples can also provide crucial alignment signal but have not been well explored yet. In this paper, we propose to utilize an attributed value encoder and partition the KG into subgraphs to model the various types of attribute triples efficiently. Besides, the performances of current EA methods are overestimated because of the name-bias of existing EA datasets. To make an objective evaluation, we propose a hard experimental setting where we select equivalent entity pairs with very different names as the test set. Under both the regular and hard settings, our method achieves significant improvements ($5.10\%$ on average Hits@$1$ in DBP$15$k) over $12$ baselines in cross-lingual and monolingual datasets. Ablation studies on different subgraphs and a case study about attribute types further demonstrate the effectiveness of our method. Source code and data can be found at https://github.com/thunlp/explore-and-evaluate.
Optimal Gradient Compression for Distributed and Federated Learning
Authors: Alyazeed Albasyoni, Mher Safaryan, Laurent Condat, Peter Richtárik
Date: 2020-10-07 00:00:00
Tasks: Federated Learning, Quantization

Communicating information, like gradient vectors, between computing nodes in distributed and federated learning is typically an unavoidable burden, resulting in scalability issues. Indeed, communication might be slow and costly. Recent advances in communication-efficient training algorithms have reduced this bottleneck by using compression techniques, in the form of sparsification, quantization, or low-rank approximation. Since compression is a lossy, or inexact, process, the iteration complexity is typically worsened; but the total communication complexity can improve significantly, possibly leading to large computation time savings. In this paper, we investigate the fundamental trade-off between the number of bits needed to encode compressed vectors and the compression error. We perform both worst-case and average-case analysis, providing tight lower bounds. In the worst-case analysis, we introduce an efficient compression operator, Sparse Dithering, which is very close to the lower bound. In the average-case analysis, we design a simple compression operator, Spherical Compression, which naturally achieves the lower bound. Thus, our new compression schemes significantly outperform the state of the art. We conduct numerical experiments to illustrate this improvement.
Learning Clusterable Visual Features for Zero-Shot Recognition
Authors: Jingyi Xu, Zhixin Shu, Dimitris Samaras
Date: 2020-10-07 00:00:00
Tasks: Classification, Few-Shot Learning, General Classification, zero-shot-classification, Zero-Shot Learning

In zero-shot learning (ZSL), conditional generators have been widely used to generate additional training features. These features can then be used to train the classifiers for testing data. However, some testing data are considered "hard" as they lie close to the decision boundaries and are prone to misclassification, leading to performance degradation for ZSL. In this paper, we propose to learn clusterable features for ZSL problems. Using a Conditional Variational Autoencoder (CVAE) as the feature generator, we project the original features to a new feature space supervised by an auxiliary classification loss. To further increase clusterability, we fine-tune the features using Gaussian similarity loss. The clusterable visual features are not only more suitable for CVAE reconstruction but are also more separable which improves classification accuracy. Moreover, we introduce Gaussian noise to enlarge the intra-class variance of the generated features, which helps to improve the classifier's robustness. Our experiments on SUN,CUB, and AWA2 datasets show consistent improvement over previous state-of-the-art ZSL results by a large margin. In addition to its effectiveness on zero-shot classification, experiments show that our method to increase feature clusterability benefits few-shot learning algorithms as well.