pwc-archive/papers-with-abstracts huggingface dataset:
True Few-Shot Learning with Prompts -- A Real-World Perspective
Authors: Timo Schick, Hinrich Schütze
Date: 2021-11-26 00:00:00
Tasks: Few-Shot Learning

Prompt-based approaches are strong at few-shot learning. However, Perez et al. (2021) have recently cast doubt on their performance because they had difficulty getting good results in a "true" few-shot setting in which prompts and hyperparameters cannot be tuned on a dev set. In view of this, we conduct an extensive study of PET, a method that combines textual instructions with example-based finetuning. We show that, if correctly configured, PET performs strongly in a true few-shot setting, i.e., without a dev set. Crucial for this strong performance is PET's ability to intelligently handle multiple prompts. We then put our findings to a real-world test by running PET on RAFT, a benchmark of tasks taken directly from realistic NLP applications for which no labeled dev or test sets are available. PET achieves a new state of the art on RAFT and performs close to non-expert humans for 7 out of 11 tasks. These results demonstrate that prompt-based learners like PET excel at true few-shot learning and underpin our belief that learning from instructions will play an important role on the path towards human-like few-shot learning capabilities.
KazNERD: Kazakh Named Entity Recognition Dataset
Authors: Rustem Yeshpanov, Yerbolat Khassanov, Huseyin Atakan Varol
Date: 2021-11-26 00:00:00
Proceeding: LREC 2022 6
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

We present the development of a dataset for Kazakh named entity recognition. The dataset was built as there is a clear need for publicly available annotated corpora in Kazakh, as well as annotation guidelines containing straightforward--but rigorous--rules and examples. The dataset annotation, based on the IOB2 scheme, was carried out on television news text by two native Kazakh speakers under the supervision of the first author. The resulting dataset contains 112,702 sentences and 136,333 annotations for 25 entity classes. State-of-the-art machine learning models to automatise Kazakh named entity recognition were also built, with the best-performing model achieving an exact match F1-score of 97.22% on the test set. The annotated dataset, guidelines, and codes used to train the models are freely available for download under the CC BY 4.0 licence from https://github.com/IS2AI/KazNERD.
Simple Contrastive Representation Adversarial Learning for NLP Tasks
Authors: Deshui Miao, JiaQi Zhang, WenBo Xie, Jian Song, Xin Li, Lijuan Jia, Ning Guo
Date: 2021-11-26 00:00:00
Tasks: Contrastive Learning, Natural Language Understanding, Self-Supervised Learning, Semantic Textual Similarity, Sentence, STS

Self-supervised learning approach like contrastive learning is attached great attention in natural language processing. It uses pairs of training data augmentations to build a classification task for an encoder with well representation ability. However, the construction of learning pairs over contrastive learning is much harder in NLP tasks. Previous works generate word-level changes to form pairs, but small transforms may cause notable changes on the meaning of sentences as the discrete and sparse nature of natural language. In this paper, adversarial training is performed to generate challenging and harder learning adversarial examples over the embedding space of NLP as learning pairs. Using contrastive learning improves the generalization ability of adversarial training because contrastive loss can uniform the sample distribution. And at the same time, adversarial training also enhances the robustness of contrastive learning. Two novel frameworks, supervised contrastive adversarial learning (SCAL) and unsupervised SCAL (USCAL), are proposed, which yields learning pairs by utilizing the adversarial training for contrastive learning. The label-based loss of supervised tasks is exploited to generate adversarial examples while unsupervised tasks bring contrastive loss. To validate the effectiveness of the proposed framework, we employ it to Transformer-based models for natural language understanding, sentence semantic textual similarity and adversarial learning tasks. Experimental results on GLUE benchmark tasks show that our fine-tuned supervised method outperforms BERT$_{base}$ over 1.75\%. We also evaluate our unsupervised method on semantic textual similarity (STS) tasks, and our method gets 77.29\% with BERT$_{base}$. The robustness of our approach conducts state-of-the-art results under multiple adversarial datasets on NLI tasks.
Ensembling of Distilled Models from Multi-task Teachers for Constrained Resource Language Pairs
Authors: Amr Hendy, Esraa A. Gad, Mohamed Abdelghaffar, Jailan S. ElMosalami, Mohamed Afify, Ahmed Y. Tawfik, Hany Hassan Awadalla
Date: 2021-11-26 00:00:00
Proceeding: WMT (EMNLP) 2021 11
Tasks: Knowledge Distillation, Translation

This paper describes our submission to the constrained track of WMT21 shared news translation task. We focus on the three relatively low resource language pairs Bengali to and from Hindi, English to and from Hausa, and Xhosa to and from Zulu. To overcome the limitation of relatively low parallel data we train a multilingual model using a multitask objective employing both parallel and monolingual data. In addition, we augment the data using back translation. We also train a bilingual model incorporating back translation and knowledge distillation then combine the two models using sequence-to-sequence mapping. We see around 70% relative gain in BLEU point for English to and from Hausa, and around 25% relative improvements for both Bengali to and from Hindi, and Xhosa to and from Zulu compared to bilingual baselines.
Near-Zero-Shot Suggestion Mining with a Little Help from WordNet
Authors: Anton Alekseev, Elena Tutubalina, Sejeong Kwon, Sergey Nikolenko
Date: 2021-11-25 00:00:00
Tasks: Suggestion mining

In this work, we explore the constructive side of online reviews: advice, tips, requests, and suggestions that users provide about goods, venues, services, and other items of interest. To reduce training costs and annotation efforts needed to build a classifier for a specific label set, we present and evaluate several entailment-based zero-shot approaches to suggestion classification in a label-fully-unseen fashion. In particular, we introduce the strategy of assigning target class labels to sentences in English language with user intentions, which significantly improves prediction quality. The proposed strategies are evaluated with a comprehensive experimental study that validated our results both quantitatively and qualitatively.
Probabilistic Impact Score Generation using Ktrain-BERT to Identify Hate Words from Twitter Discussions
Authors: Sourav Das, Prasanta Mandal, Sanjay Chatterji
Date: 2021-11-25 00:00:00

Social media has seen a worrying rise in hate speech in recent times. Branching to several distinct categories of cyberbullying, gender discrimination, or racism, the combined label for such derogatory content can be classified as toxic content in general. This paper presents experimentation with a Keras wrapped lightweight BERT model to successfully identify hate speech and predict probabilistic impact score for the same to extract the hateful words within sentences. The dataset used for this task is the Hate Speech and Offensive Content Detection (HASOC 2021) data from FIRE 2021 in English. Our system obtained a validation accuracy of 82.60%, with a maximum F1-Score of 82.68%. Subsequently, our predictive cases performed significantly well in generating impact scores for successful identification of the hate tweets as well as the hateful words from tweet pools.
A Rule-based/BPSO Approach to Produce Low-dimensional Semantic Basis Vectors Set
Authors: Atefe Pakzad, Morteza Analoui
Date: 2021-11-24 00:00:00
Tasks: Word Similarity

We intend to generate low-dimensional explicit distributional semantic vectors. In explicit semantic vectors, each dimension corresponds to a word, so word vectors are interpretable. In this research, we propose a new approach to obtain low-dimensional explicit semantic vectors. First, the proposed approach considers the three criteria Word Similarity, Number of Zero, and Word Frequency as features for the words in a corpus. Then, we extract some rules for obtaining the initial basis words using a decision tree that is drawn based on the three features. Second, we propose a binary weighting method based on the Binary Particle Swarm Optimization algorithm that obtains N_B = 1000 context words. We also use a word selection method that provides N_S = 1000 context words. Third, we extract the golden words of the corpus based on the binary weighting method. Then, we add the extracted golden words to the context words that are selected by the word selection method as the golden context words. We use the ukWaC corpus for constructing the word vectors. We use MEN, RG-65, and SimLex-999 test sets to evaluate the word vectors. We report the results compared to a baseline that uses 5k most frequent words in the corpus as context words. The baseline method uses a fixed window to count the co-occurrences. We obtain the word vectors using the 1000 selected context words together with the golden context words. Our approach compared to the Baseline method increases the Spearman correlation coefficient for the MEN, RG-65, and SimLex-999 test sets by 4.66%, 14.73%, and 1.08%, respectively.
Temporal Effects on Pre-trained Models for Language Processing Tasks
Authors: Oshin Agarwal, Ani Nenkova
Date: 2021-11-24 00:00:00
Tasks: Domain Adaptation, Experimental Design, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

Keeping the performance of language technologies optimal as time passes is of great practical interest. We study temporal effects on model performance on downstream language tasks, establishing a nuanced terminology for such discussion and identifying factors essential to conduct a robust study. We present experiments for several tasks in English where the label correctness is not dependent on time and demonstrate the importance of distinguishing between temporal model deterioration and temporal domain adaptation for systems using pre-trained representations. We find that depending on the task, temporal model deterioration is not necessarily a concern. Temporal domain adaptation however is beneficial in all cases, with better performance for a given time period possible when the system is trained on temporally more recent data. Therefore, we also examine the efficacy of two approaches for temporal domain adaptation without human annotations on new data. Self-labeling shows consistent improvement and notably, for named entity recognition, leads to better temporal adaptation than even human annotations.
For the Purpose of Curry: A UD Treebank for Ashokan Prakrit
Authors: Adam Farris, Aryaman Arora
Date: 2021-11-24 00:00:00
Proceeding: UDW (SyntaxFest) 2021 12

We present the first linguistically annotated treebank of Ashokan Prakrit, an early Middle Indo-Aryan dialect continuum attested through Emperor Ashoka Maurya's 3rd century BCE rock and pillar edicts. For annotation, we used the multilingual Universal Dependencies (UD) formalism, following recent UD work on Sanskrit and other Indo-Aryan languages. We touch on some interesting linguistic features that posed issues in annotation: regnal names and other nominal compounds, "proto-ergative" participial constructions, and possible grammaticalizations evidenced by sandhi (phonological assimilation across morpheme boundaries). Eventually, we plan for a complete annotation of all attested Ashokan texts, towards the larger goals of improving UD coverage of different diachronic stages of Indo-Aryan and studying language change in Indo-Aryan using computational methods.
Optimal Technical Indicator-based Trading Strategies Using NSGA-II
Authors: P. Shanmukh Kali Prasad, Vadlamani Madhav, Ramanuj Lal, Vadlamani Ravi
Date: 2021-11-26 00:00:00

This paper proposes non-dominated sorting genetic algorithm-II (NSGA-II ) in the context of technical indicator-based stock trading, by finding optimal combinations of technical indicators to generate buy and sell strategies such that the objectives, namely, Sharpe ratio and Maximum Drawdown are maximized and minimized respectively. NSGA-II is chosen because it is a very popular and powerful bi-objective evolutionary algorithm. The training and testing used a rolling-based approach (two years training and a year for testing) and thus the results of the approach seem to be considerably better in stable periods without major economic fluctuations. Further, another important contribution of this study is to incorporate the transaction cost and domain expertise in the whole modeling approach.
On the Difficulty of Evolving Permutation Codes
Authors: Luca Mariot, Stjepan Picek, Domagoj Jakobovic, Marko Djurasevic, Alberto Leporati
Date: 2021-11-25 00:00:00
Tasks: Evolutionary Algorithms

Combinatorial designs provide an interesting source of optimization problems. Among them, permutation codes are particularly interesting given their applications in powerline communications, flash memories, and block ciphers. This paper addresses the design of permutation codes by evolutionary algorithms (EA) by developing an iterative approach. Starting from a single random permutation, new permutations satisfying the minimum distance constraint are incrementally added to the code by using a permutation-based EA. We investigate our approach against four different fitness functions targeting the minimum distance requirement at different levels of detail and with two different policies concerning code expansion and pruning. We compare the results achieved by our EA approach to those of a simple random search, remarking that neither method scales well with the problem size.
Ranking by Momentum based on Pareto ordering of entities
Authors: Tomasz Imielinski
Date: 2021-11-25 00:00:00

Given a set of changing entities, which ones are the most uptrending over some time T? Which entities are standing out as the biggest movers? To answer this question we define the concept of momentum. Two parameters - absolute gain and relative gain over time T play the key role in defining momentum. Neither alone is sufficient since they are each biased towards a subset of entities. Absolute gain favors large entities, while relative gain favors small ones. To accommodate both absolute and relative gain in an unbiased way, we define Pareto ordering between entities. For entity E to dominate another entity F in Pareto ordering, E's absolute and relative gains over time T must be higher than F's absolute and relative gains respectively. Momentum leaders are defined as maximal elements of this partial order - the Pareto frontier. We show how to compute momentum leaders and propose linear ordering among them to help rank entities with the most momentum on the top. Additionally, we show that when vectors follow power-law, the cardinality of the set of Momentum leaders (Pareto frontier) is of the order of square root of the logarithm of the number of entities, thus it is very small.
Deriving Smaller Orthogonal Arrays from Bigger Ones with Genetic Algorithm
Authors: Luca Mariot
Date: 2021-11-25 00:00:00

We consider the optimization problem of constructing a binary orthogonal array (OA) starting from a bigger one, by removing a specified amount of lines. In particular, we develop a genetic algorithm (GA) where the underlying chromosomes are constant-weight binary strings that specify the lines to be cancelled from the starting OA. Such chromosomes are then evolved through balanced crossover and mutation operators to preserve the number of ones in them. The fitness function evaluates the matrices obtained from these chromosomes by measuring their distance from satisfying the constraints of an OA smaller than the starting one. We perform a preliminary experimental validation of the proposed genetic algorithm by crafting the initial OA as a random permutation of several blocks of the basic parity-check array, thereby guaranteeing the existence of an optimal solution.
BioLeaF: A Bio-plausible Learning Framework for Training of Spiking Neural Networks
Authors: Yukun Yang, Peng Li
Date: 2021-11-14 00:00:00

Our brain consists of biological neurons encoding information through accurate spike timing, yet both the architecture and learning rules of our brain remain largely unknown. Comparing to the recent development of backpropagation-based (BP-based) methods that are able to train spiking neural networks (SNNs) with high accuracy, biologically plausible methods are still in their infancy. In this work, we wish to answer the question of whether it is possible to attain comparable accuracy of SNNs trained by BP-based rules with bio-plausible mechanisms. We propose a new bio-plausible learning framework, consisting of two components: a new architecture, and its supporting learning rules. With two types of cells and four types of synaptic connections, the proposed local microcircuit architecture can compute and propagate error signals through local feedback connections and support training of multi-layers SNNs with a globally defined spiking error function. Under our microcircuit architecture, we employ the Spike-Timing-Dependent-Plasticity (STDP) rule operating in local compartments to update synaptic weights and achieve supervised learning in a biologically plausible manner. Finally, We interpret the proposed framework from an optimization point of view and show the equivalence between it and the BP-based rules under a special circumstance. Our experiments show that the proposed framework demonstrates learning accuracy comparable to BP-based rules and may provide new insights on how learning is orchestrated in biological systems.
A Variational Inference Approach to Inverse Problems with Gamma Hyperpriors
Authors: Shiv Agrawal, Hwanwoo Kim, Daniel Sanz-Alonso, Alexander Strang
Date: 2021-11-26 00:00:00
Tasks: Model Selection, Time Series, Time Series Analysis, Uncertainty Quantification, Variational Inference

Hierarchical models with gamma hyperpriors provide a flexible, sparse-promoting framework to bridge $L^1$ and $L^2$ regularizations in Bayesian formulations to inverse problems. Despite the Bayesian motivation for these models, existing methodologies are limited to \textit{maximum a posteriori} estimation. The potential to perform uncertainty quantification has not yet been realized. This paper introduces a variational iterative alternating scheme for hierarchical inverse problems with gamma hyperpriors. The proposed variational inference approach yields accurate reconstruction, provides meaningful uncertainty quantification, and is easy to implement. In addition, it lends itself naturally to conduct model selection for the choice of hyperparameters. We illustrate the performance of our methodology in several computed examples, including a deconvolution problem and sparse identification of dynamical systems from time series data.
Equivalence between algorithmic instability and transition to replica symmetry breaking in perceptron learning systems
Authors: Yang Zhao, Junbin Qiu, Mingshan Xie, Haiping Huang
Date: 2021-11-26 00:00:00

Binary perceptron is a fundamental model of supervised learning for the non-convex optimization, which is a root of the popular deep learning. Binary perceptron is able to achieve a classification of random high-dimensional data by computing the marginal probabilities of binary synapses. The relationship between the algorithmic instability and the equilibrium analysis of the model remains elusive. Here, we establish the relationship by showing that the instability condition around the algorithmic fixed point is identical to the instability for breaking the replica symmetric saddle point solution of the free energy function. Therefore, our analysis would hopefully provide insights towards other learning systems in bridging the gap between non-convex learning dynamics and statistical mechanics properties of more complex neural networks.
Outlier Detection for Trajectories via Flow-embeddings
Authors: Florian Frantzen, Jean-Baptiste Seby, Michael T. Schaub
Date: 2021-11-25 00:00:00
Tasks: Outlier Detection

We propose a method to detect outliers in empirically observed trajectories on a discrete or discretized manifold modeled by a simplicial complex. Our approach is similar to spectral embeddings such as diffusion-maps and Laplacian eigenmaps, that construct vertex embeddings from the eigenvectors of the graph Laplacian associated with low eigenvalues. Here we consider trajectories as edge-flow vectors defined on a simplicial complex, a higher-order generalization of graphs, and use the Hodge 1-Laplacian of the simplicial complex to derive embeddings of these edge-flows. By projecting trajectory vectors onto the eigenspace of the Hodge 1-Laplacian associated to small eigenvalues, we can characterize the behavior of the trajectories relative to the homology of the complex, which corresponds to holes in the underlying space. This enables us to classify trajectories based on simply interpretable, low-dimensional statistics. We show how this technique can single out trajectories that behave (topologically) different compared to typical trajectories, and illustrate the performance of our approach with both synthetic and empirical data.
A Kernel Test for Causal Association via Noise Contrastive Backdoor Adjustment
Authors: Robert Hu, Dino Sejdinovic, Robin J. Evans
Date: 2021-11-25 00:00:00
Tasks: Causal Inference

Causal inference grows increasingly complex as the number of confounders increases. Given treatments $X$, confounders $Z$ and outcomes $Y$, we develop a non-parametric method to test the \textit{do-null} hypothesis $H_0:\; p(y|\text{\it do}(X=x))=p(y)$ against the general alternative. Building on the Hilbert Schmidt Independence Criterion (HSIC) for marginal independence testing, we propose backdoor-HSIC (bd-HSIC) and demonstrate that it is calibrated and has power for both binary and continuous treatments under a large number of confounders. Additionally, we establish convergence properties of the estimators of covariance operators used in bd-HSIC. We investigate the advantages and disadvantages of bd-HSIC against parametric tests as well as the importance of using the do-null testing in contrast to marginal independence testing or conditional independence testing. A complete implementation can be found at \hyperlink{https://github.com/MrHuff/kgformula}{\texttt{https://github.com/MrHuff/kgformula}}.