Reviewerly/exHarmony huggingface dataset:
The Relation Between Cognitive Awareness of Reading Strategies and Writing Anxiety

This study aims to determine the cognitive awareness of reading strategies of secondary school students, the relation between the cognitive awareness of reading strategies and writing anxiety, and the common effect of some variables on cognitive awareness of reading strategies. This quantitative study is an example of a relational survey model with correlational research features. Data have been collected through two different scales. 339 secondary school students have participated in this research. Mean, frequencies, standard deviation, Pearson Correlation Coefficient, Simple Linear Regression, and Two Way Anova have been used for data analysis. As a result of the study, secondary school students have good cognitive awareness of reading strategies, and there is a negative relation between cognitive awareness of reading strategies and writing anxiety. Verbal academic success is a predictor of cognitive awareness of reading strategies, and the common effect of gender and writing anxiety doesn’t influence cognitive awareness of reading strategies.
The Correlation of Vocabulary and Sentence Mastery towards Students’ Writing Ability on Description Text

The problem raised in this study is the relationship between vocabulary and sentence mastery and the ability to compose descriptions. This research was conducted because of the reality in the field that the ability to compose descriptions and mastery of vocabulary, as well as sentences that need to be developed. This study aims to describe the relationship between vocabulary and sentence mastery and the ability to compose a description of the seventh grade students of SMPN 26 Bandar Lampung. The techniques used in collecting research data are questionnaires, tests, and observations. Research data analysis techniques include requirements analysis test, statistical analysis, and research hypothesis testing techniques. Based on the results of the study, it can be concluded that, (1) there is a positive and significant relationship between sentence structure and the ability to compose descriptions, indicated by a correlation coefficient of 0.766 and a coefficient of determination (R2) of 0.587 or 58.7%, (2) there is a relationship a positive and significant relationship between vocabulary mastery and the ability to compose descriptions indicated by a correlation coefficient of 0.662 and a coefficient of determination (R2) of 0.438 or 43.8%, (3) there is a positive and significant relationship between vocabulary mastery and sentence structure and ability composing a description shown by the correlation coefficient of 0.804 and the coefficient of determination (R2) of 0.646 or 64.6%.
Empirical analysis of power side-channel leakage of high-level synthesis designed AES circuits

&lt;span&gt;Many internet of things (IoT) devices and integrated circuit (IC) cards have been compromised by side-channel attacks. Power-analysis attacks, which identify the secret key of a cryptographic circuit by analyzing the power traces, are among the most dangerous side-channel attacks. Gen-erally, there is a trade-off between execution time and circuit area. However, the correlation between security and performance has yet to be determined. In this study, we investigate the cor-relation between side-channel attack resistance and performance (execution time and circuit area) of advanced encryption standard (AES) circuits. Eleven AES circuits with different performances are designed by high-level synthesis and logic synthesis. Of the eleven AES circuits, six are circuits with no side-channel attack countermeasures and five are circuits with masking countermeasures. We employ four metrics based on a T-test to evaluate the side-channel attack resistance. The results based on the correlation coefficient show the correlation between side-channel attack resistance and performance. The correlation varies according to four metrics or masking countermeasure. We argue that designers should change their attitudes towards circuit design when considering security.&lt;/span&gt;
Binary Embedding-based Retrieval at Tencent

Large-scale embedding-based retrieval (EBR) is the cornerstone of search-related industrial applications. Given a user query, the system of EBR aims to identify relevant information from a large corpus of documents that may be tens or hundreds of billions in size. The storage and computation turn out to be expensive and inefficient with massive documents and high concurrent queries, making it difficult to further scale up.
ISLTranslate: Dataset for Translating Indian Sign Language

Sign languages are the primary means of communication for many hard-of-hearing people worldwide.Recently, to bridge the communication gap between the hard-of-hearing community and the rest of the population, several sign language translation datasets have been proposed to enable the development of statistical sign language translation systems.However, there is a dearth of sign language resources for the Indian sign language.This resource paper introduces ISLTranslate, a translation dataset for continuous Indian Sign Language (ISL) consisting of 31k ISL-English sentence/phrase pairs.To the best of our knowledge, it is the largest translation dataset for continuous Indian Sign Language.We provide a detailed analysis of the dataset.To validate the performance of existing end-to-end Sign language to spoken language translation systems, we benchmark the created dataset with a transformer-based model for ISL translation.
Multitask Pretraining with Structured Knowledge for Text-to-SQL Generation

Many machine learning-based low-code or nocode applications involve generating code that interacts with structured knowledge.For example, one of the most studied tasks in this area is generating SQL code from a natural language statement.Prior work shows that incorporating context information from the database schema, such as table and column names, is beneficial to model performance on this task.In this work we present a large pretraining dataset and strategy for learning representations of text, tables, and SQL code that leverages the entire context of the problem.Specifically, we build on existing encoder-decoder architecture by introducing a multitask pretraining framework that complements the unique attributes of our diverse pretraining data.Our work represents the first study on large-scale pretraining of encoderdecoder models for interacting with structured knowledge, and offers a new state-of-the-art foundation model in text-to-SQL generation.We validate our approach with experiments on two SQL tasks, showing improvement over existing methods, including a 1.7 and 2.2 percentage point improvement over prior state-of-thearts on Spider and CoSQL.
Effidit: An Assistant for Improving Writing Efficiency

Shuming Shi, Enbo Zhao, Wei Bi, Deng Cai, Leyang Cui, Xinting Huang, Haiyun Jiang, Duyu Tang, Kaiqiang Song, Longyue Wang, Chenyan Huang, Guoping Huang, Yan Wang, Piji Li. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations). 2023.
Generating Text from Language Models

An increasingly large percentage of natural language processing (NLP) tasks center around the generation of text from probabilistic language models.Despite this trend, techniques for improving or specifying preferences in these generated texts rely mostly on intuition-based heuristics.Further, there lacks a unified presentation of their motivations, practical implementation, successes and pitfalls.Practitioners must, therefore, choose somewhat blindly between generation algorithms-like top-p sampling or beam search-which can lead to wildly different results.At the same time, language generation research continues to criticize and improve the standard toolboxes, further adding entropy to the state of the field.In this tutorial, we will provide a centralized and cohesive discussion of critical considerations when choosing how to generate from a language model.We will cover a wide range of empirically-observed problems (like degradation, hallucination, repetition) and their corresponding proposed algorithmic solutions from recent research (like topp sampling and its successors).We will then discuss a subset of these algorithms under a unified light; most stochastic generation strategies can be framed as locally adapting the probabilities of a model to avoid failure cases.Finally, we will then cover methods in controlled generation, that go beyond just ensuring coherence to ensure text exhibits specific desired properties.We aim for NLP practitioners and researchers to leave our tutorial with a unified framework which they can use to evaluate and contribute to the latest research in language generation.
UKP-SQuARE v3: A Platform for Multi-Agent QA Research

The continuous development of Question Answering (QA) datasets has drawn the research community's attention toward multi-domain models.A popular approach is to use multidataset models, which are models trained on multiple datasets to learn their regularities and prevent overfitting to a single dataset.However, with the proliferation of QA models in online repositories such as GitHub or Hugging Face, an alternative is becoming viable.Recent works have demonstrated that combining expert agents can yield large performance gains over multi-dataset models.To ease research in multi-agent models, we extend UKP-SQuARE, an online platform for QA research, to support three families of multi-agent systems: i) agent selection, ii) early-fusion of agents, and iii) late-fusion of agents.We conduct experiments to evaluate their inference speed and discuss the performance vs. speed trade-off compared to multi-dataset models.
NEUROSTRUCTURAL DECODING: Neural Text Generation with Structural Constraints

Text generation often involves producing texts that also satisfy a given set of semantic constraints.While most approaches for conditional text generation have primarily focused on lexical constraints, they often struggle to effectively incorporate syntactic constraints, which provide a richer language for approximating semantic constraints.We address this gap by introducing NEUROSTRUCTURAL DECODING, a new decoding algorithm that incorporates syntactic constraints to further improve the quality of the generated text.We build NEUROSTRUC-TURAL DECODING on the NeuroLogic Decoding (Lu et al., 2021b) algorithm, which enables language generation models to produce fluent text while satisfying complex lexical constraints.Our algorithm is powerful and scalable.It tracks lexico-syntactic constraints (e.g., we need to observe dog as subject and ball as object) during decoding by parsing the partial generations at each step.To this end, we adapt a dependency parser to generate parses for incomplete sentences.Our approach is evaluated on three different language generation tasks, and the results show improved performance in both lexical and syntactic metrics compared to previous methods.The results suggest this is a promising solution for integrating fine-grained controllable text generation into the conventional beam search decoding 1 .
Tab-Cleaner: Weakly Supervised Tabular Data Cleaning via Pre-training for E-commerce Catalog

Product catalogs, conceptually in the form of text-rich tables, are self-reported by individual retailers and thus inevitably contain noisy facts.Verifying such textual attributes in product catalogs is essential to improve their reliability.However, popular methods for processing free-text content, such as pre-trained language models, are not particularly effective on structured tabular data since they are typically trained on free-form natural language texts.In this paper, we present Tab-Cleaner, a model designed to handle error detection over text-rich tabular data following a pre-training / fine-tuning paradigm.We train Tab-Cleaner on a real-world Amazon Product Catalog table w.r.t millions of products and show improvements over state-of-the-art methods by 16% on PR AUC over attribute applicability classification task and by 11% on PR AUC over attribute value validation task.
Pengaruh Penerapan Model Pembelajaran Time Token terhadap Motivasi Belajar Siswa Kelas V pada Pembelajaran Tematik di Sekolah Dasar Negeri 3 Ciherang

The purpose of this study was to determine the effect of applying the time token learning model on student learning motivation: (1) How to apply the time token learning model for class v students of Elementary School Negri 3 Ciherang? (2) How is the learning motivation before and after the implementation of the time token learning model on students' learning motivation in thematic learning class v of Elementary School 3 Ciherang? (3) Is there a significant effect of the time token learning model on learning motivation in thematic learning class v of Elementary School 3 Ciherang? This study uses a type of quantitative research with an experimental method with a pre-experimental research design type one group pretest-posttest design. In this study, the population was all children at SD Negeri 3 Ciherang, Kadugede District, for the research sample, namely all students in class V, totaling 32 students. The average result of the pretest was 71.87, after the pretest was carried out then given treatment using the time token learning model to increase student learning motivation in thematic learning, during the treatment there was an increase as seen from the posttest results where the average was 79, 15. Based on the calculated t value of the SPPSS results of (4,593) and t table, a value of (2,036) is obtained so that tcount &lt; ttable, then Ho is rejected, then Ha is accepted and the results are significant, namely the significant value is less than 0.05, namely sig. (0.00) &lt; 0.05 so that Ho is rejected, then Ha is accepted, which means the use of the time token learning model affects the learning motivation of fifth grade students in thematic learning.
Adapters for Enhanced Modeling of Multilingual Knowledge and Text

Large language models appear to learn facts from the large text corpora they are trained on.Such facts are encoded implicitly within their many parameters, making it difficult to verify or manipulate what knowledge has been learned.Language models have recently been extended to multilingual language models (MLLMs), enabling knowledge to be learned across hundreds of languages.Meanwhile, knowledge graphs contain facts in an explicit triple format, which require careful and costly curation and are only available in a few highresource languages, restricting their research and application.To address these issues, we propose to enhance MLLMs with knowledge from multilingual knowledge graphs (MLKGs) so as to tackle language and knowledge graph tasks across many languages, including lowresource ones.Specifically, we introduce a lightweight adapter set to enhance MLLMs with cross-lingual entity alignment and facts from MLKGs for many languages.Experiments on common benchmarks show that such enhancement benefits both MLLMs and MLKGs, achieving: (1) comparable or improved performance for knowledge graph completion and entity alignment relative to baselines, especially for low-resource languages (for which knowledge graphs are unavailable); and (2) improved MLLM performance on language understanding tasks that require multilingual factual knowledge; all while maintaining performance on other general language tasks. 1
StoryER: Automatic Story Evaluation via Ranking, Rating and Reasoning

Existing automatic story evaluation methods place a premium on story lexical level coherence, deviating from human preference.We go beyond this limitation by considering a novel Story Evaluation method that mimics human preference when judging a story, namely StoryER, which consists of three sub-tasks: Ranking, Rating and Reasoning.Given either a machine-generated or a human-written story, StoryER requires the machine to output 1) a preference score that corresponds to human preference, 2) specific ratings and their corresponding confidences and 3) comments for various aspects (e.g., opening, character-shaping).To support these tasks, we introduce a wellannotated dataset comprising (i) 100k ranked story pairs; and (ii) a set of 46k ratings and comments on various aspects of the story.We finetune Longformer-Encoder-Decoder (LED) on the collected dataset, with the encoder responsible for preference score and aspect prediction and the decoder for comment generation.Our comprehensive experiments result in a competitive benchmark for each task, showing the high correlation to human preference.In addition, we have witnessed the joint learning of the preference scores, the aspect ratings, and the comments brings gain in each single task.Our dataset and benchmarks are publicly available to advance the research of story evaluation tasks. 1
Deep Learning on Graphs: Methods and Applications (DLG-KDD2023)

Deep Learning models are at the core of research in Artificial Intelligence research today. A tide in research for deep learning on graphs or graph neural networks. This wave of research at the intersection of graph theory and deep learning has also influenced other fields of science, including computer vision, natural language processing, program synthesis and analysis, financial security, Drug Discovery and so on. However, there are still many challenges regarding a broad range of the topics in deep learning on graphs, from methodologies to applications, and from foundations to the new frontiers of GNNs. This international workshop on "Deep Learning on Graphs: Method and Applications (DLG-KDD'23)" aims to bring together both academic researchers and industrial practitioners from different backgrounds and perspectives to above challenges.
Pre-Training to Learn in Context

In-context learning, where pre-trained language models learn to perform tasks from task examples and instructions in their contexts, has attracted much attention in the NLP community.However, the ability of in-context learning is not fully exploited because language models are not explicitly trained to learn in context.To this end, we propose PICL (Pretraining for In-Context Learning), a framework to enhance the language models' in-context learning ability by pre-training the model on a large collection of "intrinsic tasks" in the general plain-text corpus using the simple language modeling objective.PICL encourages the model to infer and perform tasks by conditioning on the contexts while maintaining task generalization of pre-trained models.We evaluate the in-context learning performance of the model trained with PICL on seven widelyused text classification datasets and the SUPER-NATURALINSTRCTIONS benchmark, which contains 100+ NLP tasks formulated to text generation.Our experiments show that PICL is more effective and task-generalizable than a range of baselines, outperforming larger language models with nearly 4x parameters.The code is publicly available at https://github.com/thu-coai/PICL.
Towards Faithful Dialogues via Focus Learning

Maintaining faithfulness between responses and knowledge is an important research topic for building reliable knowledge-grounded dialogue systems.Existing models heavily rely on the elaborate data engineering and increasing the model's parameters ignoring to track the tokens that significantly influence losses, which is decisive for the optimization direction of the model in each iteration.To address this issue, we propose Focus Learning (FocusL), a novel learning approach that adjusts the contribution of each token to the optimization direction by directly scaling the corresponding objective loss.Specifically, we first introduce a positioning method by utilizing relevance distributions between knowledge and each response token to locate knowledge-aware tokens.Then, we further design a relevance-to-weight transformation to provide dynamic token-level weights for adjusting the cross-entropy loss.Finally, we use the weighted loss to encourage the model to pay special attention to the knowledge utilization.Experimental results demonstrate that our method achieves the new state-of-the-art results and generates more reliable responses while maintaining training stability.
Knowledge Graph Embeddings using Neural Ito Process: From Multiple Walks to Stochastic Trajectories

Knowledge graphs mostly exhibit a mixture of branching relations, e.g., hasFriend, and complex structures, e.g., hierarchy and loop.Most knowledge graph embeddings have problems expressing them, because they model a specific relation r from a head h to tails by starting at the node embedding of h and transitioning deterministically to exactly one other point in the embedding space.We overcome this issue in our novel framework ItôE by modeling relations between nodes by relation-specific, stochastic transitions.Our framework is based on stochastic Itô processes, which operate on low-dimensional manifolds.ItôE is highly expressive and generic subsuming various stateof-the-art models operating on different, also non-Euclidean, manifolds.Experimental results show the superiority of ItôE over other deterministic embedding models with regard to the KG completion task.
Asymmetric feature interaction for interpreting model predictions

In natural language processing (NLP), deep neural networks (DNNs) could model complex interactions between context and have achieved impressive results on a range of NLP tasks.Prior works on feature interaction attribution mainly focus on studying symmetric interaction that only explains the additional influence of a set of words in combination, which fails to capture asymmetric influence that contributes to model prediction.In this work, we propose an asymmetric feature interaction attribution explanation model that aims to explore asymmetric higher-order feature interactions in the inference of deep neural NLP models.By representing our explanation with an directed interaction graph, we experimentally demonstrate interpretability of the graph to discover asymmetric feature interactions.Experimental results on two sentiment classification datasets show the superiority of our model against the state-of-the-art feature interaction attribution methods in identifying influential features for model predictions.
GENTLE: A Genre-Diverse Multilayer Challenge Set for English NLP and Linguistic Evaluation

We present GENTLE, a new mixed-genre English challenge corpus totaling 17K tokens and consisting of 8 unusual text types for out-ofdomain evaluation: dictionary entries, esports commentaries, legal documents, medical notes, poetry, mathematical proofs, syllabuses, and threat letters.GENTLE is manually annotated for a variety of popular NLP tasks, including syntactic dependency parsing, entity recognition, coreference resolution, and discourse parsing.We evaluate state-of-the-art NLP systems on GENTLE and find severe degradation for at least some genres in their performance on all tasks, which indicates GENTLE's utility as an evaluation dataset for NLP systems.