Reviewerly/exHarmony huggingface dataset:
The Change that Matters in Discourse Parsing: Estimating the Impact of Domain Shift on Parser Error

Discourse analysis allows us to attain inferences of a text document that extend beyond the sentence-level.The current performance of discourse models is very low on texts outside of the training distribution's coverage, diminishing the practical utility of existing models.There is need for a measure that can inform us to what extent our model generalizes from the training to the test sample when these samples may be drawn from distinct distributions.While this can be estimated via distribution shift, we argue that this does not directly correlate with change in the observed error of a classifier (i.e.error-gap).Thus, we propose to use a statistic from the theoretical domain adaptation literature which can be directly tied to error-gap.We study the bias of this statistic as an estimator of error-gap both theoretically and through a large-scale empirical study of over 2400 experiments on 6 discourse datasets from domains including, but not limited to: news, biomedical texts, TED talks, Reddit posts, and fiction.Our results not only motivate our proposal and help us to understand its limitations, but also provide insight on the properties of discourse models and datasets which improve performance in domain adaptation.For instance, we find that non-news datasets are slightly easier to transfer to than news datasets when the training and test sets are very different.Our code and an associated Python package are available to allow practitioners to make more informed model and dataset choices. 1
A non-convex PDE-constrained denoising model for impulse and Gaussian noise mixture reduction

This paper introduces a novel optimization procedure to reduce a mixture of Gaussian and impulse noise from images. This framework is based on a non-convex PDE-constrained with two diffusion operators: a local weickert and a fractional-order ones. The non-convex norm is applied to remove the impulse component, while the local and fractional operators are introduced to preserve image texture and edges. In the first part, we study the theoretical properties of the proposed PDE-constrained, and we show some well-posedness results. In a second part, after having demonstrated how to numerically find a minimizer, a proximal linearized algorithm combined with a Primal-Dual approach is introduced with local convergence results. Finally, we show extensive denoising experiments on various images and noise intensities which confirms the validity of the non-convex PDE-constrained, its analysis and also the proposed optimization procedure.
MSAMSum: Towards Benchmarking Multi-lingual Dialogue Summarization

Dialogue summarization helps users capture salient information from various types of dialogues has received much attention recently.However, current works mainly focus on English dialogue summarization, leaving other languages less well explored.Therefore, we present a multi-lingual dialogue summarization dataset, namely MSAMSum, which covers dialogue-summary pairs in six languages.Specifically, we derive MSAMSum from the standard SAMSum (Gliwa et al., 2019) using sophisticated translation techniques and further employ two methods to ensure the integral translation quality and summary factual consistency.Given the proposed MSAMum, we systematically set up five multi-lingual settings for this task, including a novel mix-lingual dialogue summarization setting.To illustrate the utility of our dataset, we benchmark various experiments with pre-trained models under different settings and report results in both supervised and zero-shot manners.We also discuss some future works towards this task to motivate future researches 1 .
Towards Improving Selective Prediction Ability of NLP Systems

It's better to say "I can't answer" than to answer incorrectly.This selective prediction ability is crucial for NLP systems to be reliably deployed in real-world applications.Prior work has shown that existing selective prediction techniques fail to perform well, especially in the out-of-domain setting.In this work, we propose a method that improves probability estimates of models by calibrating them using prediction confidence and difficulty score of instances.Using these two signals, we first annotate held-out instances and then train a calibrator to predict the likelihood of correctness of the model's prediction.We instantiate our method with Natural Language Inference (NLI) and Duplicate Detection (DD) tasks and evaluate it in both In-Domain (IID) and Outof-Domain (OOD) settings.In (IID, OOD) settings, we show that the representations learned by our calibrator result in an improvement of (15.81%, 5.64%) and (6.19%, 13.9%) over MaxProb -a selective prediction baseline-on NLI and DD tasks respectively.
A Comprehensive Analysis of Blockchain-based Cryptocurrency Mining Impact on Energy Consumption

Blockchain already has gained popularity due to its highly secured network and same time enormous computational power consumption has become an undifferentiated debate among the users.A Blockchain network is reliable, secure, transparent, and immutable where the transactions cannot be reversed between sender and receiver.Blockchain technology is not only used for mining cryptocurrency, it has other applications in different sectors like agriculture, education, insurance, etc., but the noticeable concern is still energy consumption.On the other hand, there is a significant impact on the environment due to the use of excessive energy for mining cryptocurrency which releases more carbon dioxide (CO 2 ) in nature.The Proof-of-Work (PoW) algorithm is used for mining 'Bitcoin' which is consumed enormous computational power.However, an alternative solution like Proof-of-Stake (PoS) consensus protocol has been proposed to use instead of the Proofof-Work algorithm for mining cryptocurrencies which is capable to reduce the significant amount of energy consumption.Not only that, but the use of renewable energy can also be an alternate option to use the Proof-of-Work algorithm for mining cryptocurrencies which is environment friendly.This paper aims to highlight blockchain technology, the energy consumption and impact on the environment, energy reducing method by using PoS consensus protocol instead of using the PoW algorithm, and discussion with some recommendations.
Extracting Citizen Feedback from Social Media by Appraisal Opinion Type Viewpoint

Citizen feedback is essential for improving hospitality in government policies and customer services. In this study, we propose a method for extracting citizen feedback from social media according to appraisal opinion type by filtering tweets based on multiple viewpoints such as regional dependency, status of citizen, and polarity. To improve the F1-score of the estimation of opinion unit viewpoints, we implement a multitask learning framework to estimate associated viewpoints using a BERT model. In the experiment, we focus on two domains of citizen life during the COVID-19 pandemic: nursery school life and restaurant takeout services. Our multitask learning approach was effective in estimating viewpoints on opinions. In addition, we demonstrate that citizen feedback filtering based on specific viewpoints is valuable in investigating chronological opinion transitions by appraisal opinion types.
Impact of Evaluation Methodologies on Code Summarization

There has been a growing interest in developing machine learning (ML) models for code summarization tasks, e.g., comment generation and method naming.Despite substantial increase in the effectiveness of ML models, the evaluation methodologies, i.e., the way people split datasets into training, validation, and test sets, were not well studied.Specifically, no prior work on code summarization considered the timestamps of code and comments during evaluation.This may lead to evaluations that are inconsistent with the intended use cases.In this paper, we introduce the time-segmented evaluation methodology, which is novel to the code summarization research community, and compare it with the mixed-project and cross-project methodologies that have been commonly used.Each methodology can be mapped to some use cases, and the time-segmented methodology should be adopted in the evaluation of ML models for code summarization.To assess the impact of methodologies, we collect a dataset of (code, comment) pairs with timestamps to train and evaluate several recent ML models for code summarization.Our experiments show that different methodologies lead to conflicting evaluation results.We invite the community to expand the set of methodologies used in evaluations.
Morphological Reinflection with Multiple Arguments: An Extended Annotation schema and a Georgian Case Study

In recent years, a flurry of morphological datasets had emerged, most notably UniMorph, a multi-lingual repository of inflection tables.However, the flat structure of the current morphological annotation schema makes the treatment of some languages quirky, if not impossible, specifically in cases of polypersonal agreement, where verbs agree with multiple arguments using true affixes.In this paper we propose to address this phenomenon, by expanding the UniMorph annotation schema to hierarchical feature structure that naturally accommodates complex argument marking.We apply this extended schema to one such language, Georgian, and provide a human-verified, accurate and balanced morphological dataset for Georgian verbs.The dataset has 4 times more tables and 6 times more verb forms compared to the existing UniMorph dataset, covering all possible variants of argument marking, demonstrating the adequacy of our proposed scheme.Experiments with a standard reinflection model show that generalization is easy when the data is split at the form level, but extremely hard when splitting along lemma lines.Expanding the other languages in UniMorph to this schema is expected to improve both the coverage, consistency and interpretability of this benchmark.
Efficient Cluster-Based k-Nearest-Neighbor Machine Translation-Nearest-Neighbor Machine Translation

k-Nearest-Neighbor Machine Translation (kNN-MT) has been recently proposed as a non-parametric solution for domain adaptation in neural machine translation (NMT).It aims to alleviate the performance degradation of advanced MT systems in translating out-ofdomain sentences by coordinating with an additional token-level feature-based retrieval module constructed from in-domain data.Previous studies (Khandelwal et al., 2021; Zheng et al., 2021a) have already demonstrated that non-parametric NMT is even superior to models fine-tuned on out-of-domain data.In spite of this success, kNN retrieval is at the expense of high latency, in particular for large datastores.To make it practical, in this paper, we explore a more efficient kNN-MT and propose to use clustering to improve the retrieval efficiency.Concretely, we first propose a cluster-based Compact Network for feature reduction in a contrastive learning manner to compress context features into 90+% lower dimensional vectors.We then suggest a cluster-based pruning solution to filter out 10%~40% redundant nodes in large datastores while retaining translation quality.Our proposed methods achieve better or comparable performance while reducing up to 57% inference latency against the advanced non-parametric MT model on several machine translation benchmarks.Experimental results indicate that the proposed methods maintain the most useful information of the original datastore and the Compact Network shows good generalization on unseen domains.Codes are available at https:
Deep Reinforcement Learning for Resource Management in Blockchain-Enabled Federated Learning Network

Blockchain-enabled Federated Learning (BFL) enables model updates to be stored in blockchain in a reliable manner. However, one problem is the increase of the training latency due to the mining process. Moreover, mobile devices have energy and CPU constraints. Therefore, the machine learning model owner (MLMO) needs to decide the data and energy that the mobile devices use for the training and determine the block generation rate to minimize the system latency and mining cost while achieving the target accuracy. Under the uncertainty of BFL, we propose to use deep reinforcement learning to find the optimal decisions for the MLMO.
A large-scale computational study of content preservation measures for text style transfer and paraphrase generation

Text style transfer and paraphrasing of texts are actively growing areas of NLP, dozens of methods for solving these tasks have been recently introduced.In both tasks, the system is supposed to generate a text which should be semantically similar to the input text.Therefore, these tasks are dependent on methods of measuring textual semantic similarity.However, it is still unclear which measures are the best to automatically evaluate content preservation between original and generated text.According to our observations, many researchers still use BLEU-like measures, while there exist more advanced measures including neuralbased that significantly outperform classic approaches.The current problem is the lack of a thorough evaluation of the available measures.We close this gap by conducting a large-scale computational study by comparing 57 measures based on different principles on 19 annotated datasets.We show that measures based on cross-encoder models outperform alternative approaches in almost all cases.We also introduce the Mutual Implication Score (MIS), a measure that uses the idea of paraphrasing as a bidirectional entailment and outperforms all other measures on the paraphrase detection task and performs on par with the best measures in the text style transfer task.
Traffic Sign Detection Based on Improved YOLOv3 in Foggy Environment

Abstract Aiming at the problem of poor detection accuracy and inaccurate positioning of traffic signs under foggy conditions, this paper proposes an improved YOLOv3 detection algorithm. Firstly, a data set of Chinese traffic signs in a foggy environment is constructed; The dark channel a priori algorithm based on guided filtering is used to process the image with fog, which overcomes the problem of image quality degradation caused by fog. Mosaic data enhancement is performed on the annotated data set image, which speeds up the convergence speed of the network. Increased the feature scale of YOLOv3 algorithm. The loss function of the network is optimized, CIOU is used as the positioning loss, and the positioning accuracy is improved. At the same time, the method of transfer learning is used to overcome the problem of insufficient samples. The enhanced yolov3 algorithm proposed in this paper has higher detection accuracy and shorter detection time than the standard yolov3 algorithm and SSD algorithm.
Parameter-Efficient Abstractive Question Answering over Tables or Text

A long-term ambition of information seeking question answering (QA) systems is to reason over multi-modal contexts and generate natural answers to user queries.Today, memory intensive pre-trained language models are adapted to downstream tasks such as QA by fine-tuning the model on QA data in a specific modality like unstructured text or structured tables.To avoid training such memoryhungry models while utilizing a uniform architecture for each modality, parameter-efficient adapters add and train small task-specific bottleneck layers between transformer layers.In this work, we study parameter-efficient abstractive QA in encoder-decoder models over structured tabular data and unstructured textual data using only 1.5% additional parameters for each modality.We also ablate over adapter layers in both encoder and decoder modules to study the efficiency-performance trade-off and demonstrate that reducing additional trainable parameters down to 0.7%-1.0%leads to comparable results.Our models out-perform current stateof-the-art models on tabular QA datasets such as Tablesum and FeTaQA, and achieve comparable performance on a textual QA dataset such as NarrativeQA using significantly less trainable parameters than fine-tuning.
BERT-based Approach to Arabic Hate Speech and Offensive Language Detection in Twitter: Exploiting Emojis and Sentiment Analysis

The user-generated content on the internet including that on social media may contain offensive language and hate speech which negatively affect the mental health of the whole internet society and may lead to hate crimes.Intelligent models for automatic detection of offensive language and hate speech have attracted significant attention recently.In this paper, we propose an automatic method for detecting offensive language and fine-grained hate speech from Arabic tweets.We compare between BERT and two conventional machine learning techniques (SVM, logistic regression).We also investigate the use of sentiment analysis and emojis descriptions as appending features along with the textual content of the tweets.The experiments shows that BERT-based model gives the best results, surpassing the best benchmark systems in the literature, on all three tasks: (a) offensive language detection with 84.3% F1-score, (b) hate speech detection with 81.8% F1-score, and (c) fine-grained hatespeech recognition (e.g., race, religion, social class, etc.) with 45.1% F1-score.The use of sentiment analysis slightly improves the performance of the models when detecting offensive language and hate speech but has no positive effect on the performance of the models when recognising the type of the hate speech.The use of textual emoji description as features can improve or deteriorate the performance of the models depending on the size of the examples per class and whether the emojis are considered among distinctive features between classes or not.
Primum Non Nocere: Before working with Indigenous data, the ACL must confront ongoing colonialism

In this paper, we challenge the ACL community to reckon with historical and ongoing colonialism by adopting a set of ethical obligations and best practices drawn from the Indigenous studies literature.While the vast majority of NLP research focuses on a very small number of very high resource languages (English, Chinese, etc), some work has begun to engage with Indigenous languages.No research involving Indigenous language data can be considered ethical without first acknowledging that Indigenous languages are not merely very low resource languages.The toxic legacy of colonialism permeates every aspect of interaction between Indigenous communities and outside researchers.Ethical research must actively challenge this colonial legacy by actively acknowledging and opposing its continuing presence, and by explicitly acknowledging and centering Indigenous community goals and Indigenous ways of knowing.To this end, we propose that the ACL draft and adopt an ethical framework for NLP researchers and computational linguists wishing to engage in research involving Indigenous languages.
SciNLI: A Corpus for Natural Language Inference on Scientific Text

Existing Natural Language Inference (NLI) datasets, while being instrumental in the advancement of Natural Language Understanding (NLU) research, are not related to scientific text.In this paper, we introduce SCINLI, a large dataset for NLI that captures the formality in scientific text and contains 107, 412 sentence pairs extracted from scholarly papers on NLP and computational linguistics.Given that the text used in scientific literature differs vastly from the text used in everyday language both in terms of vocabulary and sentence structure, our dataset is well suited to serve as a benchmark for the evaluation of scientific NLU models.Our experiments show that SCINLI is harder to classify than the existing NLI datasets.Our best performing model with XLNet achieves a Macro F1 score of only 78.18% and an accuracy of 78.23% showing that there is substantial room for improvement.
Open-domain Dialogue Generation: What We Can Do, Cannot Do, And Should Do Next

Human-computer conversation has long been an interest of artificial intelligence and natural language processing research.Recent years have seen a dramatic improvement in quality for both task-oriented and open-domain dialogue systems, and an increasing amount of research in the area.The goal of this work is threefold: (1) to provide an overview of recent advances in the field of open-domain dialogue, (2) to summarize issues related to ethics, bias, and fairness that the field has identified as well as typical errors of dialogue systems, and (3) to outline important future challenges.We hope that this work will be of interest to both new and experienced researchers in the area.
Factors That Affect Digital Innovation Sustainability among SMEs in the Middle East Region

Digital innovation in small and medium-sized enterprises (SMEs) has been studied extensively over the past quarter-century. The products, processes, and/or model innovation in businesses that are viewed as novel necessitate significant alterations from consumers and are represented in or facilitated by IT; this is what it means when digital innovation is mentioned. Nonetheless, despite the unique application of digital innovation in different organizations, the factors that lead to its success are still uncertain. Therefore, this study aims at evaluating the various factors that affect the digital innovation of SMEs in the Middle East region. This research used a descriptive research design in which primary data from the SME owners and employees were used to evaluate the research objectives. Descriptive statistics, such as frequencies and structural equation models conducted through SPSS, were used to evaluate the research objectives in showing how various factors such as the Internet of Things (IoT; physical objects and things with sensors, software, and other technological techniques integrated into them for the purpose of exchanging data and connecting with other systems and devices), digital platforms, and digital orientation have influenced digital innovation sustainability in SMEs. Results showed a positive and substantial impact on digital innovation sustainability as a result of the IoT, digital platforms, and digital orientation. In conclusion, the research shows that SMEs should use digital platforms, the IoT, and digital orientation to be digitally stable.
Time-Optimal Trajectory Planning with Interaction with the Environment

Optimal motion planning along prescribed paths can be solved with several techniques, but most of them do not take into account the wrenches exerted by the end-effector when in contact with the environment. When a dynamic model of the environment is not available, no consolidated methodology exists to consider the effect of the interaction. Regardless of the specific performance index to optimize, this article proposes a strategy to include external wrenches in the optimal planning algorithm, considering the task specifications. This procedure is instantiated for minimum-time trajectories and validated on a real robot performing an interaction task under admittance control. The results prove that the inclusion of end-effector wrenches affect the planned trajectory, in fact modifying the manipulator's dynamic capability.