Reviewerly/exHarmony huggingface dataset:
A guide to the dataset explosion in QA, NLI, and commonsense reasoning

Question answering, natural language inference and commonsense reasoning are increasingly popular as general NLP system benchmarks, driving both modeling and dataset work.Only for question answering we already have over 100 datasets, with over 40 published after 2018.However, most new datasets get "solved" soon after publication, and this is largely due not to the verbal reasoning capabilities of our models, but to annotation artifacts and shallow cues in the data that they can exploit.This tutorial aims to (1) provide an up-to-date guide to the recent datasets, (2) survey the old and new methodological issues with dataset construction, and (3) outline the existing proposals for overcoming them.The target audience is the NLP practitioners who are lost in dozens of the recent datasets, and would like to know what these datasets are actually measuring.Our overview of the problems with the current datasets and the latest tips and tricks in the dataset construction methodology will also be useful to the researchers working on future benchmarks.The tutorial slides are available online at https://www.annargrs.github.io/dataset-explosion.The tutorial will be of the cutting-edge type.The tutorial slides are available online at https://www.annargrs.github.io/dataset-explosion.Prerequisites.We assume basic familiarity with the standard machine learning evaluation workflow and the three tasks that we are covering (question answering, commonsense reasoning, natural language inference).We also assume some familiarity with the methodology of crowdsourcing NLP datasets.
Taking Profound Offence Seriously: Freedom of Speech v. Human Dignity

This study discusses the issue of offence to sensibilities as possible grounds for limiting freedom of expression. This issue is constantly brought to the public agenda, as for example, in the limitation imposed in Israel on the playing of Richard Wagner’s music by Israeli orchestras. The proposed thesis is as follows: The Offence to Sensibilities Argument will take precedence over free expression only in cases where profound and direct damage is inflicted upon the sensibilities of individuals or a target group, undermining their dignity, especially when the speaker’s intentions are to offend the target individual/group under circumstances in which the individual or target group cannot avoid. In every case, it is incumbent upon those who wish to limit expression to examine the content of the expression and its manner, the duration and intensity of the expression, the frequency of the offence, the speaker’s intentions, the circumstances and the likely consequences of the offensive speech. Psychologists should be consulted concerning the severity of each offence.
Semibinomial conditionally nonlinear autoregressive models of discrete random sequences: probabilistic properties and statistical parameter estimation

Abstract We introduce a new model ${\mathscr{P}}$ -CNAR( s ) of sequences of discrete random variables with long memory determined by semibinomial conditionally nonlinear autoregression of order s ∈ ℕ with small number of parameters. Probabilistic properties of this model are studied. For parameters of the model ${\mathscr{P}}$ -CNAR a family of consistent asymptotically normal statistical FB-estimates is suggested and the existence of an efficient FB-estimate is proved. Computational advantages of FB-estimate w.r.t. maximum likelihood estimate are shown: less restrictive sufficient conditions for uniqueness, explicit form of FB-estimate, fast recursive computation algorithm under extension of the model ${\mathscr{P}}$ -CNAR. Subfamily of “sparse” FB-estimates that use some subset of frequencies of s -tuples is constructed, the asymptotic variance minimization problem within this subfamily is solved.
Context-aware Adaptive Recommendation System for Personal Well-being Services

Nowadays, a healthy lifestyle is an essential requirement in people's daily life. Although well-being recommendation systems have been extensively explored in different domains, there are still some challenges for developing efficient recommendation systems dealing with the limitations of content-based recommendation approaches. In this paper, a context-aware adaptive recommendation system is proposed to provide personal wellbeing services intended to help people to have a healthy lifestyle in Ambient Assisted Living (AAL) systems. The recommendations are based on people's behaviors. Machine-learning models are firstly used to recognize human activities, locations, and objects. The different contexts of human behaviors, including location, object, frequency, duration, and sequences of frequent activities, are then extracted. An ontology, called Human ActiVity ONtology (HAVON) ontology, is used to conceptualize human activities and their contexts. Finally, a probabilistic version of Answer set Programming (ASP), a high-level expressive logic-based formalism, is proposed to provide adaptive recommendations through a set of probabilistic rules based on human behaviors. A companion robot, called Pepper, is used for the evaluation of the proposed recommendation system. The evaluation results demonstrate the ability of the proposed system to help people to have a healthy lifestyle.
Diverse dialogue generation with context dependent dynamic loss function

Dialogue systems using deep learning have achieved generation of fluent response sentences to user utterances. Nevertheless, they tend to produce responses that are not diverse and which are less context-dependent. To address these shortcomings, we propose a new loss function, an Inverse N-gram loss (INF), which incorporates contextual fluency and diversity at the same time by a simple formula. Our INF loss can adjust its loss dynamically by a weight using the inverse frequency of the tokens’ n-gram applied to Softmax Cross-Entropy loss, so that rare tokens appear more likely while retaining the fluency of the generated sentences. We trained Transformer using English and Japanese Twitter replies as single-turn dialogues using different loss functions. Our INF loss model outperformed the baselines of SCE loss and ITF loss models in automatic evaluations such as DIST-N and ROUGE, and also achieved higher scores on our human evaluations of coherence and richness.
Venses @ AcCompl-It: Computing Complexity vs Acceptability with a Constituent Trigram Model and Semantics

In this paper we present work carried out for the Ac-ComplIt task. ItVENSES is a system for syntactic and semantic processing that is based on the parser for Italian called ItGetaruns to analyse each sentence. In previous EVALITA tasks we only used semantics to produce the results. In this year EVALITA, we used both a statistically based approach and the semantic one used previously. The statistic approach is characterized by the use of trigrams of constituents computed by the system and checked against a trigram model derived from the constituency version of VIT – Venice Italian Treebank. Results measured in term of a correlation, are not particularly high, below 50% the Acceptability task and slightly over 30% the Complexity one.
Exploiting Narrative Context and A Priori Knowledge of Categories in Textual Emotion Classification

Recognition of the mental state of a human character in text is a major challenge in natural language processing. In this study, we investigate the efficacy of the narrative context in recognizing the emotional states of human characters in text and discuss an approach to make use of a priori knowledge regarding the employed emotion category system. Specifically, we experimentally show that the accuracy of emotion classification is substantially increased by encoding the preceding context of the target sentence using a BERT-based text encoder. We also compare ways to incorporate a priori knowledge of emotion categories by altering the loss function used in training, in which our proposal of multi-task learning that jointly learns to classify positive/negative polarity of emotions is included. The experimental results suggest that, when using Plutchik’s Wheel of Emotions, it is better to jointly classify the basic emotion categories with positive/negative polarity rather than directly exploiting its characteristic structure in which eight basic categories are arranged in a wheel.
Reducing Student’s English Dialogue Anxiety in Online Learning through Board Game

The COVID 19 has been going on since April 2020 in Indonesia. It has resulted in the Indonesian government, especially the Ministry of Education and Culture, taking preventive steps to minimize contracting the virus. One of them is using online learning for students to go to school. However, there are challenges for teachers and students while learning in an online class, such as students who are often absent, students will quickly feel bored, students tend to be passive in the learning process, students do not master the learning material, and much more. It also happens in an English lesson, especially a dialogue activity. This research aims to prove whether using a board game can be effective media to reduce students’ language anxiety level or not during English learning in an online classroom. The kind of board game is the combination between snake and ladder and candy land, or it has been modified based on learning purpose. The research design used in this research is a quasi-trial non-equivalent control group design. The samples of this research are 60 ninth grades of junior high school students which are divided into the trial and control group. Data analyses used in this research are using SPSS mean score, data deviation, while hypothesis testing used in this research is the Mann-Whitney test. The result showed that a board game can reduce students’ anxiety levels. The data showed that the mean score of students after implementing board game reduces by 2.90 points, while the standard deviation increases by 2.901 points. From the Mann-Whitney test, it is known that Asymp. Sig. (2-tailed) is 0.003 or &lt; 0.05 which shows that H0 is rejected, and H1 is accepted. From the data analysis, it can be shown that the board game is effective media to reduce students’ language anxiety in dialogue activity during learning in an online class. Keywords: board game, language anxiety, online learning
A Survey of Unsupervised Dependency Parsing

Syntactic dependency parsing is an important task in natural language processing.Unsupervised dependency parsing aims to learn a dependency parser from sentences that have no annotation of their correct parse trees.Despite its difficulty, unsupervised parsing is an interesting research direction because of its capability of utilizing almost unlimited unannotated text data.It also serves as the basis for other research in low-resource parsing.In this paper, we survey existing approaches to unsupervised dependency parsing, identify two major classes of approaches, and discuss recent trends.We hope that our survey can provide insights for researchers and facilitate future research on this topic.
Using COVID-19 Vaccine Efficacy Data to Teach One-Sample Hypothesis Testing

In late November 2020, there was a flurry of media coverage of two companies’ claims of 95% efficacy rates of newly developed COVID-19 vaccines, but information about the confidence interval was not reported. This paper presents a way of teaching the concept of hypothesis testing and the construction of confidence intervals using numbers announced by the drug makers Pfizer and Moderna publicized by the media. Instead of a two-sample test or more complicated statistical models, we use the elementary one-proportion z -test to analyze the data. The method is designed to be accessible for students who have only taken a one-semester elementary statistics course. We will justify the use of a z -distribution as an approximation for the confidence interval of the efficacy rate. Bayes’s rule will be applied to relate the probability of being in the vaccine group among the volunteers who were infected by COVID-19 to the more consequential probability of being infected by COVID-19 given that the person is vaccinated.
The TALP-UPC system description for WMT20 news translation task: multilingual adaptation for low resource MT

In this article, we describe the TALP-UPC participation in the WMT20 news translation shared task for Tamil-English. Given the low amount of parallel training data, we resort to adapt the task to a multilingual system to benefit from the positive transfer from high resource languages. We use iterative backtranslation to fine-tune the system and benefit from the monolingual data available. In order to measure the effectivity of such methods, we compare our results to a bilingual baseline system.
Tencent AI Lab Machine Translation Systems for the WMT20 Biomedical Translation Task.

This paper describes the Tencent AI Lab submission of the WMT2020 shared task on biomedical translation in four language directions: German English, English German, Chinese English and English Chinese. We implement our system with model ensemble technique on different transformer architectures (Deep, Hybrid, Big, Large Transformers). To enlarge the in-domain bilingual corpus, we use back-translation of monolingual in-domain data in the target language as additional in-domain training data. Our systems in German->English and English->German are ranked 1st and 3rd respectively according to the official evaluation results in terms of BLEU scores.
A Tokenization System for the Kurdish Language.

Tokenization is one of the essential and fundamental tasks in natural language processing. Despite the recent advances in applying unsupervised statistical methods for this task, every language with its writing system and orthography represents specific challenges that should be addressed individually. In this paper, as a preliminary study of its kind, we propose an approach for the tokenization of the Sorani and Kurmanji dialects of Kurdish using a lexicon and a morphological analyzer. We demonstrate how the morphological complexity of the language along with the lack of a unified orthography can be efficiently addressed in tokenization. We also develop an annotated dataset for which our approach outperforms the performance of unsupervised methods.
On the Redundancy in the Rank of Neural Network Parameters and Its Controllability

In this paper, we show that parameters of a neural network can have redundancy in their ranks, both theoretically and empirically. When viewed as a function from one space to another, neural networks can exhibit feature correlation and slower training due to this redundancy. Motivated by this, we propose a novel regularization method to reduce the redundancy in the rank of parameters. It is a combination of an objective function that makes the parameter rank-deficient and a dynamic low-rank factorization algorithm that gradually reduces the size of this parameter by fusing linearly dependent vectors together. This regularization-by-pruning approach leads to a neural network with better training dynamics and fewer trainable parameters. We also present experimental results that verify our claims. When applied to a neural network trained to classify images, this method provides statistically significant improvement in accuracy and 7.1 times speedup in terms of number of steps required for training. Furthermore, this approach has the side benefit of reducing the network size, which led to a model with 30.65% fewer trainable parameters.
Ain’t no party like a LibGuides Party: ’cause a LibGuides Party is mandatory

What is currently on your librarian backburner? We’d be willing to make a few bets on the content of those pots growing cold. Maybe we would find a weeding project on pause, a draft of an article gathering dust, or that meeting agenda you know you can throw together only hours before the actual meeting. LibGuides maintenance would probably be found on most of our backburners, too.When the academic year begins and the instruction requests, committee meetings, and consultations begin to crowd our calendars, LibGuides upkeep can easily fall by the wayside. Updating broken links, tweaking code for enhanced accessibility, and cleaning up our asset lists can be tedious, and often uninspiring work. Anyone who has faced down a long list of broken links can likely attest to this.
Stabilized Medical Image Attacks

Convolutional Neural Networks (CNNs) have advanced existing medical systems for automatic disease diagnosis. However, a threat to these systems arises that adversarial attacks make CNNs vulnerable. Inaccurate diagnosis results make a negative influence on human healthcare. There is a need to investigate potential adversarial attacks to robustify deep medical diagnosis systems. On the other side, there are several modalities of medical images (e.g., CT, fundus, and endoscopic image) of which each type is significantly different from others. It is more challenging to generate adversarial perturbations for different types of medical images. In this paper, we propose an image-based medical adversarial attack method to consistently produce adversarial perturbations on medical images. The objective function of our method consists of a loss deviation term and a loss stabilization term. The loss deviation term increases the divergence between the CNN prediction of an adversarial example and its ground truth label. Meanwhile, the loss stabilization term ensures similar CNN predictions of this example and its smoothed input. From the perspective of the whole iterations for perturbation generation, the proposed loss stabilization term exhaustively searches the perturbation space to smooth the single spot for local optimal escape. We further analyze the KL-divergence of the proposed loss function and find that the loss stabilization term makes the perturbations updated towards a fixed objective spot while deviating from the ground truth. This stabilization ensures the proposed medical attack effective for different types of medical images while producing perturbations in small variance. Experiments on several medical image analysis benchmarks including the recent COVID-19 dataset show the stability of the proposed method.
CLaC at SMM4H 2020: Birth Defect Mention Detection

For the detection of personal tweets, where a parent speaks of a child’s birth defect, CLaC combines ELMo word embeddings and gazetteer lists from external resources with a GCNN (for encoding dependencies), in a multi layer, transformer inspired architecture. To address the task, we compile several gazetteer lists from resources such as MeSH and GI. The proposed system obtains .69 for μF1 score in the SMM4H 2020 Task 5 where the competition average is .65.
Ações de competência em informação voltadas para as bibliotecas escolares da rede municipal de ensino de Vila Velha – ES

: This article aims to analyze the contributions of the school library network in Vila Velha (ES) with regard to actions for the development of information literacy in students. It is a descriptive research, in which a pre-analysis of the projects of this network of libraries was carried out, selecting a sample with six, which included actions of information literacy. Then, an analysis was undertaken from the four dimensions of information literacy. As a result, it was found that the projects have aspects related to the technical, aesthetic, ethical and political dimensions. It is considered that these actions enable Basic Education students to access and use information, as well as the ability to perform a critical analysis of its contents. It is also possible to offer subsidies for the planning of future projects by the aforementioned library and for others that are interested in the theme.
Formal Analysis of a Neural Network Predictor inShared-Control Autonomous Driving

Autonomous driving systems may encounter scenarios where it is necessary to transfer control to the human driver, for instance when encountering unpredictable dangerous road conditions. To be able to do so safely, the autonomous system needs an estimate of how long it will take for the human driver to take control of the vehicle. Deep neural networks can be used for making such predictions, however proving that neural networks meet critical safety requirements presents a challenge. We present a formally verified neural network which predicts "Takeover-time" in a shared-control autonomous driving system. The network is trained on data collected from a (semi-)autonomous driving simulator. We use Marabou (a formal verification tool), to analyze the network’s sensitivity, local robustness, contextual robustness, and to find adversarial inputs which produce unsafe outputs.
A dual-encoding system for dialect classification.

In this paper we present the architecture, processing pipeline and results of the ensemble model developed for Romanian Dialect Identification task. The ensemble model consists of two TF-IDF encoders and a deep learning model aimed together at classifying input samples based on the writing patterns which are specific to each of the two dialects. Although the model performs well on the training set, its performance degrades heavily on the evaluation set. The drop in performance is due to the design decision which makes the model put too much weight on presence/lack of textual marks when determining the sample label.
UPDeT: Universal Multi-agent RL via Policy Decoupling with Transformers

Recent advances in multi-agent reinforcement learning have been largely limited in training one model from scratch for every new task. The limitation is due to the restricted model architecture related to fixed input and output dimensions. This hinders the experience accumulation and transfer of the learned agent over tasks with diverse levels of difficulty (e.g. 3 vs 3 or 5 vs 6 multi-agent games). In this paper, we make the first attempt to explore a universal multi-agent reinforcement learning pipeline, designing one single architecture to fit tasks with the requirement of different observation and action configurations. Unlike previous RNN-based models, we utilize a transformer-based model to generate a flexible policy by decoupling the policy distribution from the intertwined input observation with an importance weight measured by the merits of the self-attention mechanism. Compared to a standard transformer block, the proposed model, named as Universal Policy Decoupling Transformer (UPDeT), further relaxes the action restriction and makes the multi-agent task's decision process more explainable. UPDeT is general enough to be plugged into any multi-agent reinforcement learning pipeline and equip them with strong generalization abilities that enables the handling of multiple tasks at a time. Extensive experiments on large-scale SMAC multi-agent competitive games demonstrate that the proposed UPDeT-based multi-agent reinforcement learning achieves significant results relative to state-of-the-art approaches, demonstrating advantageous transfer capability in terms of both performance and training speed (10 times faster).