pwc-archive/papers-with-abstracts huggingface dataset:
Fool Me Twice: Entailment from Wikipedia Gamification
Authors: Julian Martin Eisenschlos, Bhuwan Dhingra, Jannis Bulian, Benjamin Börschinger, Jordan Boyd-Graber
Date: 2021-04-10 00:00:00
Proceeding: NAACL 2021 4
Tasks: Retrieval

We release FoolMeTwice (FM2 for short), a large dataset of challenging entailment pairs collected through a fun multi-player game. Gamification encourages adversarial examples, drastically lowering the number of examples that can be solved using "shortcuts" compared to other popular entailment datasets. Players are presented with two tasks. The first task asks the player to write a plausible claim based on the evidence from a Wikipedia page. The second one shows two plausible claims written by other players, one of which is false, and the goal is to identify it before the time runs out. Players "pay" to see clues retrieved from the evidence pool: the more evidence the player needs, the harder the claim. Game-play between motivated players leads to diverse strategies for crafting claims, such as temporal inference and diverting to unrelated evidence, and results in higher quality data for the entailment and evidence retrieval tasks. We open source the dataset and the game code.
Not All Attention Is All You Need
Authors: Hongqiu Wu, Hai Zhao, Min Zhang
Date: 2021-04-10 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: All, Document Classification, Named Entity Recognition (NER), Natural Language Understanding

Beyond the success story of pre-trained language models (PrLMs) in recent natural language processing, they are susceptible to over-fitting due to unusual large model size. To this end, dropout serves as a therapy. However, existing methods like random-based, knowledge-based and search-based dropout are more general but less effective onto self-attention based models, which are broadly chosen as the fundamental architecture of PrLMs. In this paper, we propose a novel dropout method named AttendOut to let self-attention empowered PrLMs capable of more robust task-specific tuning. We demonstrate that state-of-the-art models with elaborate training design may achieve much stronger results. We verify the universality of our approach on extensive natural language processing tasks.
ShadowGNN: Graph Projection Neural Network for Text-to-SQL Parser
Authors: Zhi Chen, Lu Chen, Yanbin Zhao, Ruisheng Cao, Zihan Xu, Su Zhu, Kai Yu
Date: 2021-04-10 00:00:00
Proceeding: NAACL 2021 4
Tasks: Decoder, Semantic Parsing, Text to SQL, Text-To-SQL

Given a database schema, Text-to-SQL aims to translate a natural language question into the corresponding SQL query. Under the setup of cross-domain, traditional semantic parsing models struggle to adapt to unseen database schemas. To improve the model generalization capability for rare and unseen schemas, we propose a new architecture, ShadowGNN, which processes schemas at abstract and semantic levels. By ignoring names of semantic items in databases, abstract schemas are exploited in a well-designed graph projection neural network to obtain delexicalized representation of question and schema. Based on the domain-independent representations, a relation-aware transformer is utilized to further extract logical linking between question and schema. Finally, a SQL decoder with context-free grammar is applied. On the challenging Text-to-SQL benchmark Spider, empirical results show that ShadowGNN outperforms state-of-the-art models. When the annotated data is extremely limited (only 10\% training set), ShadowGNN gets over absolute 5\% performance gain, which shows its powerful generalization ability. Our implementation will be open-sourced at \url{https://github.com/WowCZ/shadowgnn}.
Lookup-Table Recurrent Language Models for Long Tail Speech Recognition
Authors: W. Ronny Huang, Tara N. Sainath, Cal Peyser, Shankar Kumar, David Rybach, Trevor Strohman
Date: 2021-04-09 00:00:00
Tasks: GPU, Language Modeling, Language Modelling, Sentence, speech-recognition, Speech Recognition

We introduce Lookup-Table Language Models (LookupLM), a method for scaling up the size of RNN language models with only a constant increase in the floating point operations, by increasing the expressivity of the embedding table. In particular, we instantiate an (additional) embedding table which embeds the previous n-gram token sequence, rather than a single token. This allows the embedding table to be scaled up arbitrarily -- with a commensurate increase in performance -- without changing the token vocabulary. Since embeddings are sparsely retrieved from the table via a lookup; increasing the size of the table adds neither extra operations to each forward pass nor extra parameters that need to be stored on limited GPU/TPU memory. We explore scaling n-gram embedding tables up to nearly a billion parameters. When trained on a 3-billion sentence corpus, we find that LookupLM improves long tail log perplexity by 2.44 and long tail WER by 23.4% on a downstream speech recognition task over a standard RNN language model baseline, an improvement comparable to a scaling up the baseline by 6.2x the number of floating point operations.
UPB at SemEval-2021 Task 8: Extracting Semantic Information on Measurements as Multi-Turn Question Answering
Authors: Andrei-Marius Avram, George-Eduard Zaharia, Dumitru-Clementin Cercel, Mihai Dascalu
Date: 2021-04-09 00:00:00
Proceeding: SEMEVAL 2021
Tasks: Question Answering, Relation Extraction

Extracting semantic information on measurements and counts is an important topic in terms of analyzing scientific discourses. The 8th task of SemEval-2021: Counts and Measurements (MeasEval) aimed to boost research in this direction by providing a new dataset on which participants train their models to extract meaningful information on measurements from scientific texts. The competition is composed of five subtasks that build on top of each other: (1) quantity span identification, (2) unit extraction from the identified quantities and their value modifier classification, (3) span identification for measured entities and measured properties, (4) qualifier span identification, and (5) relation extraction between the identified quantities, measured entities, measured properties, and qualifiers. We approached these challenges by first identifying the quantities, extracting their units of measurement, classifying them with corresponding modifiers, and afterwards using them to jointly solve the last three subtasks in a multi-turn question answering manner. Our best performing model obtained an overlapping F1-score of 36.91% on the test set.
Self-Adjusting Population Sizes for Non-Elitist Evolutionary Algorithms: Why Success Rates Matter
Authors: Mario Alejandro Hevia Fajardo, Dirk Sudholt
Date: 2021-04-12 00:00:00
Tasks: Evolutionary Algorithms

Evolutionary algorithms (EAs) are general-purpose optimisers that come with several parameters like the sizes of parent and offspring populations or the mutation rate. It is well known that the performance of EAs may depend drastically on these parameters. Recent theoretical studies have shown that self-adjusting parameter control mechanisms that tune parameters during the algorithm run can provably outperform the best static parameters in EAs on discrete problems. However, the majority of these studies concerned elitist EAs and we do not have a clear answer on whether the same mechanisms can be applied for non-elitist EAs. We study one of the best-known parameter control mechanisms, the one-fifth success rule, to control the offspring population size $\lambda$ in the non-elitist $(1,\lambda)$ EA. It is known that the $(1,\lambda)$ EA has a sharp threshold with respect to the choice of $\lambda$ where the expected runtime on the benchmark function OneMax changes from polynomial to exponential time. Hence, it is not clear whether parameter control mechanisms are able to find and maintain suitable values of $\lambda$. For OneMax we show that the answer crucially depends on the success rate $s$ (i.\,e.\ a one-$(s+1)$-th success rule). We prove that, if the success rate is appropriately small, the self-adjusting $(1,\lambda)$ EA optimises OneMax in $O(n)$ expected generations and $O(n \log n)$ expected evaluations, the best possible runtime for any unary unbiased black-box algorithm. A small success rate is crucial: we also show that if the success rate is too large, the algorithm has an exponential runtime on OneMax and other functions with similar characteristics.
Epigenetic evolution of deep convolutional models
Authors: Alexander Hadjiivanov, Alan Blair
Date: 2021-04-12 00:00:00
Tasks: image-classification, Image Classification

In this study, we build upon a previously proposed neuroevolution framework to evolve deep convolutional models. Specifically, the genome encoding and the crossover operator are extended to make them applicable to layered networks. We also propose a convolutional layer layout which allows kernels of different shapes and sizes to coexist within the same layer, and present an argument as to why this may be beneficial. The proposed layout enables the size and shape of individual kernels within a convolutional layer to be evolved with a corresponding new mutation operator. The proposed framework employs a hybrid optimisation strategy involving structural changes through epigenetic evolution and weight update through backpropagation in a population-based setting. Experiments on several image classification benchmarks demonstrate that the crossover operator is sufficiently robust to produce increasingly performant offspring even when the parents are trained on only a small random subset of the training dataset in each epoch, thus providing direct confirmation that learned features and behaviour can be successfully transferred from parent networks to offspring in the next generation.
Adaptive conversion of real-valued input into spike trains
Authors: Alexander Hadjiivanov
Date: 2021-04-12 00:00:00

This paper presents a biologically plausible method for converting real-valued input into spike trains for processing with spiking neural networks. The proposed method mimics the adaptive behaviour of retinal ganglion cells and allows input neurons to adapt their response to changes in the statistics of the input. Thus, rather than passively receiving values and forwarding them to the hidden and output layers, the input layer acts as a self-regulating filter which emphasises deviations from the average while allowing the input neurons to become effectively desensitised to the average itself. Another merit of the proposed method is that it requires only one input neuron per variable, rather than an entire population of neurons as in the case of the commonly used conversion method based on Gaussian receptive fields. In addition, since the statistics of the input emerge naturally over time, it becomes unnecessary to pre-process the data before feeding it to the network. This enables spiking neural networks to process raw, non-normalised streaming data. A proof-of-concept experiment is performed to demonstrate that the proposed method operates as expected.
An error-propagation spiking neural network compatible with neuromorphic processors
Authors: Matteo Cartiglia, Germain Haessig, Giacomo Indiveri
Date: 2021-04-12 00:00:00
Tasks: Edge-computing, Event-based vision

Spiking neural networks have shown great promise for the design of low-power sensory-processing and edge-computing hardware platforms. However, implementing on-chip learning algorithms on such architectures is still an open challenge, especially for multi-layer networks that rely on the back-propagation algorithm. In this paper, we present a spike-based learning method that approximates back-propagation using local weight update mechanisms and which is compatible with mixed-signal analog/digital neuromorphic circuits. We introduce a network architecture that enables synaptic weight update mechanisms to back-propagate error signals across layers and present a network that can be trained to distinguish between two spike-based patterns that have identical mean firing rates, but different spike-timings. This work represents a first step towards the design of ultra-low power mixed-signal neuromorphic processing systems with on-chip learning circuits that can be trained to recognize different spatio-temporal patterns of spiking activity (e.g. produced by event-based vision or auditory sensors).
Parallel integrative learning for large-scale multi-response regression with incomplete outcomes
Authors: Ruipeng Dong, Daoji Li, Zemin Zheng
Date: 2021-04-11 00:00:00
Tasks: Multi-Task Learning, regression, Variable Selection

Multi-task learning is increasingly used to investigate the association structure between multiple responses and a single set of predictor variables in many applications. In the era of big data, the coexistence of incomplete outcomes, large number of responses, and high dimensionality in predictors poses unprecedented challenges in estimation, prediction, and computation. In this paper, we propose a scalable and computationally efficient procedure, called PEER, for large-scale multi-response regression with incomplete outcomes, where both the numbers of responses and predictors can be high-dimensional. Motivated by sparse factor regression, we convert the multi-response regression into a set of univariate-response regressions, which can be efficiently implemented in parallel. Under some mild regularity conditions, we show that PEER enjoys nice sampling properties including consistency in estimation, prediction, and variable selection. Extensive simulation studies show that our proposal compares favorably with several existing methods in estimation accuracy, variable selection, and computation efficiency.
CovNet: Covariance Networks for Functional Data on Multidimensional Domains
Authors: Soham Sarkar, Victor M. Panaretos
Date: 2021-04-11 00:00:00

Covariance estimation is ubiquitous in functional data analysis. Yet, the case of functional observations over multidimensional domains introduces computational and statistical challenges, rendering the standard methods effectively inapplicable. To address this problem, we introduce "Covariance Networks" (CovNet) as a modeling and estimation tool. The CovNet model is "universal" - it can be used to approximate any covariance up to desired precision. Moreover, the model can be fitted efficiently to the data and its neural network architecture allows us to employ modern computational tools in the implementation. The CovNet model also admits a closed-form eigendecomposition, which can be computed efficiently, without constructing the covariance itself. This facilitates easy storage and subsequent manipulation of a covariance in the context of the CovNet. We establish consistency of the proposed estimator and derive its rate of convergence. The usefulness of the proposed method is demonstrated by means of an extensive simulation study and an application to resting state fMRI data.
Fatigued PageRank
Authors: José Devezas, Sérgio Nunes
Date: 2021-04-12 00:00:00
Tasks: Retrieval

Connections among entities are everywhere. From social media interactions to web page hyperlinks, networks are frequently used to represent such complex systems. Node ranking is a fundamental task that provides the strategy to identify central entities according to multiple criteria. Popular node ranking metrics include degree, closeness or betweenness centralities, as well as HITS authority or PageRank. In this work, we propose a novel node ranking metric, where we combine PageRank and the idea of node fatigue, in order to model a random explorer who wants to optimize coverage - it gets fatigued and avoids previously visited nodes. We formalize and exemplify the computation of Fatigued PageRank, evaluating it as a node ranking metric, as well as query-independent evidence in ad hoc document retrieval. Based on the Simple English Wikipedia link graph with clickstream transitions from the English Wikipedia, we find that Fatigued PageRank is able to surpass both indegree and HITS authority, but only for the top ranking nodes. On the other hand, based on the TREC Washington Post Corpus, we were unable to outperform the BM25 baseline, obtaining similar performance for all graph-based metrics, except for indegree, which lowered GMAP and MAP, but increased NDCG@10 and P@10.
Fatigued Random Walks in Hypergraphs: A Neuronal Analogy to Improve Retrieval Performance
Authors: José Devezas, Sérgio Nunes
Date: 2021-04-12 00:00:00
Tasks: Retrieval

Hypergraphs are data structures capable of capturing supra-dyadic relations. We can use them to model binary relations, but also to model groups of entities, as well as the intersections between these groups or the contained subgroups. In previous work, we explored the usage of hypergraphs as an indexing data structure, in particular one that was capable of seamlessly integrating text, entities and their relations to support entity-oriented search tasks. As more information is added to the hypergraph, however, it not only increases in size, but it also becomes denser, making the task of efficiently ranking nodes or hyperedges more complex. Random walks can effectively capture network structure, without compromising performance, or at least providing a tunable balance between efficiency and effectiveness, within a nondeterministic universe. For a higher effectiveness, a higher number of random walks is usually required, which often results in lower efficiency. Inspired by von Neumann and the neuron in the brain, we propose and study the usage of node and hyperedge fatigue as a way to temporarily constrict random walks during keyword-based ad hoc retrieval. We found that we were able to improve search time by a factor of 32, but also worsen MAP by a factor of 8. Moreover, by distinguishing between fatigue in nodes and hyperedges, we are able to find that, for hyperedge ranking tasks, we consistently obtained lower MAP scores when increasing fatigue for nodes. On the other hand, the overall impact of hyperedge fatigue was slightly positive, although it also slightly worsened efficiency.
Generating Code with the Help of Retrieved Template Functions and Stack Overflow Answers
Authors: Dawn Drain, Changran Hu, Chen Wu, Mikhail Breslav, Neel Sundaresan
Date: 2021-04-12 00:00:00
Tasks: Code Search, Retrieval

We approach the important challenge of code autocompletion as an open-domain task, in which a sequence-to-sequence code generator model is enhanced with the ability to attend to reference code snippets supplied by a semantic code search engine. In this work, we present a novel framework to precisely retrieve template functions as well as intent-snippet pairs and effectively train such a retrieval-guided code generator. To demonstrate the effectiveness of our model designs, we perform extensive experiments with CodeSearchNet which contains template functions and CoNaLa which contains Stack Overflow intent-snippet pairs. We also investigate different retrieval models, including Elasticsearch, DPR, and our fusion representation search model, which currently holds the number one spot on the CodeSearchNet leaderboard. We observe improvements by leveraging multiple database elements and further gain from retrieving diverse data points by using Maximal Marginal Relevance. Overall, we see a 4% improvement to cross-entropy loss, a 15% improvement to edit distance, and a 44% improvement to BLEU score when retrieving template functions. We see subtler improvements of 2%, 11%, and 6% respectively when retrieving Stack Overflow intent-snippet pairs. We also create a novel Stack Overflow-Function Alignment dataset, which consists of 150K tuples of functions and Stack Overflow intent-snippet pairs that are of help in writing the associated function, of which 1.7K are manually curated.
A Probabilistic Framework for Lexicon-based Keyword Spotting in Handwritten Text Images
Authors: E. Vidal, A. H. Toselli, J. Puigcerver
Date: 2021-04-09 00:00:00
Tasks: Benchmarking, Keyword Spotting

Query by String Keyword Spotting (KWS) is here considered as a key technology for indexing large collections of handwritten text images to allow fast textual access to the contents of these collections. Under this perspective, a probabilistic framework for lexicon-based KWS in text images is presented. The presentation aims at providing a tutorial view that helps to understand the relations between classical statements of KWS and the relative challenges entailed by these statements. More specifically, the development of the proposed framework makes it self-evident that word recognition or classification implicitly or explicitly underlies any formulation of KWS. Moreover, it clearly suggests that the same statistical models and training methods successfully used for handwriting text recognition can advantageously be used also for KWS, even though KWS does not generally require or rely on any kind of previously produced image transcripts. These ideas are developed into a specific, probabilistically sound approach for segmentation-free, lexicon-based, query-by-string KWS. Experiments carried out using this approach are presented, which support the consistency and general interest of the proposed framework. Several datasets, traditionally used for KWS benchmarking are considered, with results significantly better than those previously published for these datasets. In addition, results on two new, larger handwritten text image datasets are reported, showing the great potential of the methods proposed in this paper for indexing and textual search in large collections of handwritten documents.
Spatial Feature Calibration and Temporal Fusion for Effective One-stage Video Instance Segmentation
Authors: Minghan Li, Shuai Li, Lida Li, Lei Zhang
Date: 2021-04-06 00:00:00
Proceeding: CVPR 2021 1
Tasks: Instance Segmentation, Segmentation, Semantic Segmentation, valid, Video Instance Segmentation

Modern one-stage video instance segmentation networks suffer from two limitations. First, convolutional features are neither aligned with anchor boxes nor with ground-truth bounding boxes, reducing the mask sensitivity to spatial location. Second, a video is directly divided into individual frames for frame-level instance segmentation, ignoring the temporal correlation between adjacent frames. To address these issues, we propose a simple yet effective one-stage video instance segmentation framework by spatial calibration and temporal fusion, namely STMask. To ensure spatial feature calibration with ground-truth bounding boxes, we first predict regressed bounding boxes around ground-truth bounding boxes, and extract features from them for frame-level instance segmentation. To further explore temporal correlation among video frames, we aggregate a temporal fusion module to infer instance masks from each frame to its adjacent frames, which helps our framework to handle challenging videos such as motion blur, partial occlusion and unusual object-to-camera poses. Experiments on the YouTube-VIS valid set show that the proposed STMask with ResNet-50/-101 backbone obtains 33.5 % / 36.8 % mask AP, while achieving 28.6 / 23.4 FPS on video instance segmentation. The code is released online https://github.com/MinghanLi/STMask.