Reviewerly/exHarmony huggingface dataset:
Annotation-Inspired Implicit Discourse Relation Classification with Auxiliary Discourse Connective Generation

Implicit discourse relation classification is a challenging task due to the absence of discourse connectives.To overcome this issue, we design an end-to-end neural model to explicitly generate discourse connectives for the task, inspired by the annotation process of PDTB.Specifically, our model jointly learns to generate discourse connectives between arguments and predict discourse relations based on the arguments and the generated connectives.To prevent our relation classifier from being misled by poor connectives generated at the early stage of training while alleviating the discrepancy between training and inference, we adopt Scheduled Sampling to the joint learning.We evaluate our method on three benchmarks, PDTB 2.0, PDTB 3.0, and PCC.Results show that our joint model significantly outperforms various baselines on three datasets, demonstrating its superiority for the task.
Improving Neural Machine Translation Formality Control with Domain Adaptation and Reranking-based Transductive Learning

This paper presents Huawei Translation Service Center (HW-TSC)'s submission on the IWSLT 2023 formality control task, which provides two training scenarios: supervised and zero-shot, each containing two language pairs, and sets constrained and unconstrained conditions.We train the formality control models for these four language pairs under these two conditions respectively, and submit the corresponding translation results.Our efforts are divided into two fronts: enhancing general translation quality and improving formality control capability.According to the different requirements of the formality control task, we use a multi-stage pre-training method to train a bilingual or multilingual neural machine translation (NMT) model as the basic model, which can improve the general translation quality of the base model to a relatively high level.Then, under the premise of affecting the general translation quality of the basic model as little as possible, we adopt domain adaptation and rerankingbased transductive learning methods to improve the formality control capability of the model.
Controllable Text Generation via Probability Density Estimation in the Latent Space

Previous work on controllable text generation has explored the idea of control from the latent space, such as optimizing a representation with attribute-specific classifiers or sampling one from relevant discrete samples.However, they cannot effectively model a complex space with diverse attributes, high dimensionality, and asymmetric structure, leaving subsequent controls unsatisfying.In this work, we propose a novel control framework using probability density estimation in the latent space.Our method utilizes an invertible transformation function, the Normalizing Flow, that maps the complex distributions in the latent space to simple Gaussian distributions in the prior space.Thus, we can perform sophisticated and flexible controls in the prior space and feed the control effects back into the latent space owing to the bijection property of invertible transformations.Experiments on single-attribute and multi-attribute control reveal that our method outperforms several strong baselines on attribute relevance and text quality, achieving a new SOTA.Further analysis of control strength adjustment demonstrates the flexibility of our control strategy 1 .
CREST: A Joint Framework for Rationalization and Counterfactual Text Generation

Selective rationales and counterfactual examples have emerged as two effective, complementary classes of interpretability methods for analyzing and training NLP models.However, prior work has not explored how these methods can be integrated to combine their complementary advantages.We overcome this limitation by introducing CREST (ContRastive Edits with Sparse raTionalization), a joint framework for selective rationalization and counterfactual text generation, and show that this framework leads to improvements in counterfactual quality, model robustness, and interpretability.First, CREST generates valid counterfactuals that are more natural than those produced by previous methods, and subsequently can be used for data augmentation at scale, reducing the need for human-generated examples.Second, we introduce a new loss function that leverages CREST counterfactuals to regularize selective rationales and show that this regularization improves both model robustness and rationale quality, compared to methods that do not leverage CREST counterfactuals.Our results demonstrate that CREST successfully bridges the gap between selective rationales and counterfactual examples, addressing the limitations of existing methods and providing a more comprehensive view of a model's predictions.
Logical Neural Networks for Knowledge Base Completion with Embeddings &amp; Rules

Knowledge base completion (KBC) has benefitted greatly by learning explainable rules in an human-interpretable dialect such as first-order logic.Rule-based KBC has so far, mainly focussed on learning one of two types of rules: conjunction-of-disjunctions and disjunction-ofconjunctions.We qualitatively show, via examples, that one of these has an advantage over the other when it comes to achieving high quality KBC.To the best of our knowledge, we are the first to propose learning both kinds of rules within a common framework.To this end, we propose to utilize logical neural networks (LNN) (Riegel et al., 2020), a powerful neurosymbolic AI framework that can express both kinds of rules and learn these end-to-end using gradient-based optimization.Our in-depth experiments show that our LNN-based approach to learning rules for KBC leads to roughly 10% relative improvements, if not more, over SotA rule-based KBC methods.Moreover, by showing how to combine our proposed methods with knowledge graph embeddings we further achieve additional 7.5% relative improvement.
Multi-level Distillation of Semantic Knowledge for Pre-training Multilingual Language Model

Pre-trained multilingual language models play an important role in cross-lingual natural language understanding tasks.However, existing methods did not focus on learning the semantic structure of representation, and thus could not optimize their performance.In this paper, we propose Multi-level Multilingual Knowledge Distillation (MMKD), a novel method for improving multilingual language models.Specifically, we employ a teacher-student framework to adopt rich semantic representation knowledge in English BERT.We propose token-, word-, sentence-, and structure-level alignment objectives to encourage multiple levels of consistency between source-target pairs and correlation similarity between teacher and student models.We conduct experiments on crosslingual evaluation benchmarks including XNLI, PAWS-X, and XQuAD.Experimental results show that MMKD outperforms other baseline models of similar size on XNLI and XQuAD and obtains comparable performance on PAWS-X.Especially, MMKD obtains significant performance gains on low-resource languages.
Adversarial Concept Erasure in Kernel Space

The representation space of neural models for textual data emerges in an unsupervised manner during training.Understanding how those representations encode human-interpretable concepts is a fundamental problem.One prominent approach for the identification of concepts in neural representations is searching for a linear subspace whose erasure prevents the prediction of the concept from the representations.However, while many linear erasure algorithms are tractable and interpretable, neural networks do not necessarily represent concepts in a linear manner.To identify non-linearly encoded concepts, we propose a kernelization of a linear minimax game for concept erasure.We demonstrate that it is possible to prevent specific nonlinear adversaries from predicting the concept.However, the protection does not transfer to different nonlinear adversaries.Therefore, exhaustively erasing a non-linearly encoded concept remains an open problem.
BanglaRQA: A Benchmark Dataset for Under-resourced Bangla Language Reading Comprehension-based Question Answering with Diverse Question-Answer Types

High-resource languages, such as English, have access to a plethora of datasets with various question-answer types resembling realworld reading comprehension.However, there is a severe lack of diverse and comprehensive question-answering datasets in underresourced languages like Bangla.The ones available are either translated versions of English datasets with a niche answer format or created by human annotations focusing on a specific domain, question type, or answer type.To address these limitations, this paper introduces BanglaRQA, a reading comprehensionbased Bangla question-answering dataset with various question-answer types.BanglaRQA consists of 3,000 context passages and 14,889 question-answer pairs created from those passages.The dataset comprises answerable and unanswerable questions covering four unique categories of questions and three types of answers.In addition, this paper also implemented four different Transformer models for question-answering on the proposed dataset.The best-performing model achieved an overall 62.42% EM and 78.11% F1 score.However, detailed analyses showed that the performance varies across question-answer types, leaving room for substantial improvement of the model performance.Furthermore, we demonstrated the effectiveness of BanglaRQA as a training resource by showing strong results on the bn_squad dataset.Therefore, BanglaRQA has the potential to contribute to the advancement of future research by enhancing the capability of language models.
Mixed-modality Representation Learning and Pre-training for Joint Table-and-Text Retrieval in OpenQA

Retrieving evidences from tabular and textual resources is essential for open-domain question answering (OpenQA), which provides more comprehensive information.However, training an effective dense table-text retriever is difficult due to the challenges of table-text discrepancy and data sparsity problem.To address the above challenges, we introduce an optimized OpenQA Table-TExt Retriever (OTTER) to jointly retrieve tabular and textual evidences.Firstly, we propose to enhance mixed-modality representation learning via two mechanisms: modality-enhanced representation and mixedmodality negative sampling strategy.Secondly, to alleviate data sparsity problem and enhance the general retrieval ability, we conduct retrieval-centric mixed-modality synthetic pre-training.Experimental results demonstrate that OTTER substantially improves the performance of table-and-text retrieval on the OTT-QA dataset.Comprehensive analyses examine the effectiveness of all the proposed mechanisms.Besides, equipped with OTTER, our OpenQA system achieves the state-of-the-art result on the downstream QA task, with 10.1% absolute performance gain in terms of the exact match over the previous best system.
stopes - Modular Machine Translation Pipelines

Pierre Andrews, Guillaume Wenzek, Kevin Heffernan, Onur Çelebi, Anna Sun, Ammar Kamran, Yingzhe Guo, Alexandre Mourachko, Holger Schwenk, Angela Fan. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. 2022.
Searching for explanations of black-box classifiers in the space of semantic queries

Deep learning models have achieved impressive performance in various tasks, but they are usually opaque with regards to their inner complex operation, obfuscating the reasons for which they make decisions. This opacity raises ethical and legal concerns regarding the real-life use of such models, especially in critical domains such as in medicine, and has led to the emergence of the eXplainable Artificial Intelligence (XAI) field of research, which aims to make the operation of opaque AI systems more comprehensible to humans. The problem of explaining a black-box classifier is often approached by feeding it data and observing its behaviour. In this work, we feed the classifier with data that are part of a knowledge graph, and describe the behaviour with rules that are expressed in the terminology of the knowledge graph, that is understandable by humans. We first theoretically investigate the problem to provide guarantees for the extracted rules and then we investigate the relation of “explanation rules for a specific class” with “semantic queries collecting from the knowledge graph the instances classified by the black-box classifier to this specific class”. Thus we approach the problem of extracting explanation rules as a semantic query reverse engineering problem. We develop algorithms for solving this inverse problem as a heuristic search in the space of semantic queries and we evaluate the proposed algorithms on four simulated use-cases and discuss the results.
PENGARUH PEMBELAJARAN BERDIFERENSIASI TERHADAP KEMAMPUAN COMPUTATIONAL THINKING SISWA SEKOLAH DASAR

Education Transformation in Indonesia is now demanded to be faster and able to produce students who are not only proficient in reading and writing but also students who have skills in matters directly related to problem solving in real life. The ability to think critically, apply creativity, work together, solve problems and be communicative are some of the abilities that are the main capital in 21th Century Learning besides other abilities such as reading, writing science and mathematics. The purpose of this study is in accordance with the existing problem formulation, namely to determine the effect of differentiated learning on the computational thinking abilities of elementary school students. In this study the authors used a quantitative approach with quasi-experimental research methods. The research design used was Time Series Design Intervention Analysis. The selection of the method and design of this study took into account the implementation of repeated treatments. The results of testing the hypothesis using the paired sample t-test from the pre-test and post-test data in series 1, 2, and 3 show the results t-count&gt; t-table. In the series one t-count obtained is 7.319. In series 2 the t-count got a value of 9.729 and in series 3 the t-count was 11.660. The results obtained were that the Computational Thinking ability of elementary school students had increased after being treated with Differentiated Learning.
A Multiple Mechanism Enhanced Arithmetic Optimization Algorithm for Numerical Problems

The Arithmetic Optimization Algorithm (AOA) is a meta-heuristic algorithm inspired by mathematical operators, which may stagnate in the face of complex optimization issues. Therefore, the convergence and accuracy are reduced. In this paper, an AOA variant called ASFAOA is proposed by integrating a double-opposite learning mechanism, an adaptive spiral search strategy, an offset distribution estimation strategy, and a modified cosine acceleration function formula into the original AOA, aiming to improve the local exploitation and global exploration capability of the original AOA. In the proposed ASFAOA, a dual-opposite learning strategy is utilized to enhance population diversity by searching the problem space a lot better. The spiral search strategy of the tuna swarm optimization is introduced into the addition and subtraction strategy of AOA to enhance the AOA’s ability to jump out of the local optimum. An offset distribution estimation strategy is employed to effectively utilize the dominant population information for guiding the correct individual evolution. In addition, an adaptive cosine acceleration function is proposed to perform a better balance between the exploitation and exploration capabilities of the AOA. To demonstrate the superiority of the proposed ASFAOA, two experiments are conducted using existing state-of-the-art algorithms. First, The CEC 2017 benchmark function was applied with the aim of evaluating the performance of ASFAOA on the test function through mean analysis, convergence analysis, stability analysis, Wilcoxon signed rank test, and Friedman’s test. The proposed ASFAOA is then utilized to solve the wireless sensor coverage problem and its performance is illustrated by two sets of coverage problems with different dimensions. The results and discussion show that ASFAOA outperforms the original AOA and other comparison algorithms. Therefore, ASFAOA is considered as a useful technique for practical optimization problems.
PENGARUH MOTIVASI BELAJAR DALAM PEMBELAJARAN BERDIFERENSIASI TERHADAP HASIL BELAJAR BAHASA INDONESIA SISWA MAN 1 PULANG PISAU

Components that influence student learning success are those that originate within students (interest and motivation) and from outside students such as the environment, social, teacher learning methods, and so on. Interest in learning Indonesian at MAN 1 Pulang Pisau is categorized as low due to a lack of student learning motivation, so there are still many low grades. Even though this school is one of the favorite schools with a large enough number of students because it is the only Islamic-based school and has been accredited A. The purpose of this study is to describe how motivation influences learning on student learning outcomes of MAN 1 Pulang Pisau, by applying differentiated learning designed by the teacher Indonesian. This research method is descriptive qualitative method. The population in this study were all students of class X and the sample in the study was class X IPS II MAN I Pulang Pisau. Data collection techniques were carried out by observing learning in class and distributing questionnaires. The data taken is the result of student learning in the form of written tests and questionnaire results which are distributed in the form of a questionnaire. Then the data is described descriptively and a final conclusion is drawn. Based on the results of the study, students' learning motivation in differentiated learning can improve student learning outcomes. Students are more active and enthusiastic in participating in learning, more confident, and able to respect others.
Redundancy-Free High-Performance Dynamic GNN Training with Hierarchical Pipeline Parallelism

Temporal Graph Neural Networks(TGNNs) extend the success of Graph Neural Networks to dynamic graphs. Distributed TGNN training requires efficiently tackling temporal dependency, which often leads to excessive cross-device communication that generates significant redundant data. However, existing systems are unable to remove the redundancy in data reuse and transfer, and suffer from severe communication overhead in a distributed setting. This paper presents Sven, an algorithm and system co-designed TGNN training library for the end-to-end performance optimization on multi-node multi-GPU systems. Exploiting dependency patterns of TGNN models and characteristics of dynamic graph datasets, we design redundancy-free data organization and load-balancing partitioning strategies that mitigate the redundant data communication and evenly partition dynamic graphs at the vertex level. Furthermore, we develop a hierarchical pipeline mechanism integrating data prefetching, micro-batch pipelining, and asynchronous pipelining to mitigate the communication overhead. As the first scaling study on the memory-based TGNNs training, experiments conducted on an HPC cluster of 64 GPUs show that Sven can achieve up to 1.7x-3.3x speedup over the state-of-art approaches and a factor of up to 5.26x communication efficiency improvement.
Distance-dependent emission spectrum from two qubits in a strong-coupling regime

We study the emission spectrum of two distant qubits strongly coupled to a waveguide by using the numerical and analytical approaches, which are beyond the Markovian approximation and the rotating-wave approximation. The numerical approach combines the Dirac-Frenkel time-dependent variational principle with the multiple Davydov ${D}_{1}$ ansatz. A transformed rotating-wave approximation (TRWA) treatment and a standard perturbation (SP) are used to analytically calculate the emission spectrum. It is found that the variational approach and the TRWA treatment yield accurate emission spectra of the two distant qubits in certain strong-coupling regimes while the SP breaks down. The emission spectrum is found to be asymmetric irrespective of the two-qubit distance and exhibits a single peak, double peaks, and multiple peaks depending on the two-qubit distance as well as the initial states. In sharp contrast with the single-qubit case, the excited-state populations of the two qubits can ultraslowly decay due to the subradiance even in the presence of a strong qubit-waveguide coupling, which in turn yields an ultranarrow emission line. Our results provide insights into the emission spectral features of the two distant qubits in the strong light-matter coupling regime.
Human Intelligence to Artificial Intelligence: Critical thinking and Emotional Intelligence

Artificial Intelligence may be it is branch of computing. The computer science engineering is creating intelligent machines and especially intelligent computer programs. AI could also be how of making a computer. A computer-controlled robot or software thinks intelligently. AI is accomplished by studying the way to think human brain. How humans learn, decide, and work while trying to unravel a haul. We also create intelligent machines. It is often developing human activities. Some human activities are reading, listening, each and are going to be covered. Machine learning, deep learning may be a part of this AI. The AI projects have a machine control and remote. Mostly AI is machine learning. Current status will be there from AI. Feature goals in AI. Computing system study that plan to model and applying human brain. It is a branch of computer simulating a intelligent behavior. The machine capacity is intimate human behavior. The intelligence has some types are going to be there, linguistic intelligence, musical intelligence, logical-mathematical intelligence, spatial intelligence, intra-personal intelligence, interpersonal intelligence. The intelligence composed by reasoning, learning, problem solving, perception, linguistic intelligence and so on. Ability to calculate a system, reasoning, perceives a relationships and analogs, learn from experience. Store and retrieve information from memory. Solving a problems and complex ideas to use tongue fluently classifying. Generalize and adapt new situation
Resisting the Edge-Type Disturbance for Link Prediction in Heterogeneous Networks

The rapid development of heterogeneous networks has proposed new challenges to the long-standing link prediction problem. Existing models trained on the verified edge samples from different types usually learn type-specific knowledge, and their type-specific predictions may be contradictory for unverified edge samples with uncertain types. This challenge is termed edge-type disturbance in link prediction in heterogeneous networks. To address this challenge, we develop a disturbance-resilient prediction method ( DRPM ) comprising a structural characterizer, a type differentiator, and a resilient predictor. The structural characterizer is responsible for learning edge representations for link prediction. Concurrently, the type differentiator distinguishes type-specific edge representations to generate diverse type experts while maximizing their link prediction performances on specific types. Furthermore, the resilient predictor evaluates the reliability weights of different type experts to develop a resilient prediction mechanism to aggregate discriminable predictions. Extensive experiments conducted on various real-world datasets demonstrate the importance of the explainable introduction of the edge-type disturbance and the superiority of DRPM over state-of-the-art methods.