Reviewerly/exHarmony huggingface dataset:
Types of Flouting Maxims analysis in Jurassic World Dominion Movie

Flouting maxims present as pragmatics phenomena that can be easily found in society. It comes in the communication that does not obey the cooperative principles. In this research, flouting maxims in utterances uttered by characters of movie was analyzed. The types of flouting maxims became the object of this research. The researchers used Jurassic World Dominion movie to be the data source of this descriptive qualitative research. Flouting maxims utterances were collected as data and those were analyzed by using the theory of Grice (1975). Observational method and technique of non-participatory were used to collect the flouting maxims utterances. In analyzing the data, the researchers applied pragmatic identity method and pragmatic competence- in equalizing technique. The results showed that there were 15 data found in Jurassic World Dominion movie. The 15 data consisted of 7 data of flouting maxim of relation , 3 data of flouting maxim of quality, 3 data of flouting maxim of quantity, and 2 data for maxim of manner
Translation Errors in Subtitle of the Movie Joker 2019 on Netflix

There have been various genres of movies completed with their subtitles. The role of subtitle is crucial, besides gaining engagement with the audience, subtitle also conveys the meaning and the message of the movie. However, there have been found errors in the subtitle itself. This study aims to discover the types of translation errors in the English-Indonesian language pair of the Joker (2019) movie shown on Netflix. The English-Indonesian subtitles were used as the data for the study. The method of the study is qualitative descriptive and The American Translators Association (ATA) framework errors category was applied in analyzing the error types. The results showed that there were six types of errors; (1) omission errors; (2) misunderstanding errors; (3) faithfulness errors; (4) literalness errors; (5) ambiguity errors and (6) other errors that do not include in the category. The most frequent error is omission, misunderstanding, and faithfulness errors. All the errors found influenced the information and message delivered to the viewers.
Research on the Fourteenth National Games Effect on Tennis Course Based on Social Embedding Theory

The Fourteenth Games in 2021 was successfully held in Shaanxi Province inside which many universities also played a more important role in the Fourteenth Games, and were affected by the Games in turn. This article, by adopting such research methods as the literature and data method, questionnaire survey method, interview method, logic analysis method, and comprehensive induction method, analyzes students’ insight into the Games and how they conduct physical exercises after the Games. And by using the theory of social embedding to study effects of the Games on tennis courses of colleges and universities. Through research, with the help of social embedding theory, the embedded subject of this article is the Effects of the Fourteenth National Games. The embedded object is P.E of Universities. The embedded content is the effects such as sports culture and sports incentives brought about by the Games. The subjects of behavior in embedding process are college students. The Games have exerted aspects of impact on college sports including the sports spirit such as the competition, the sports culture, and the hard work contained in the sports courses are embedded in the physical education courses. When this embedding process is stable and lasts long, the Games will have a profound impact on college physical education courses, and it will also affect the colleges and universities as well as their college sports courses, Students 'attitudes and behaviors have promoted the teaching of tennis curriculum, promoted students' post -school exercises, and achieved tennis popularization. It also helps to promote the construction of sports culture and sports spirit in colleges and universities.
<scp>PyScribe</scp>–Learning to describe python code

Abstract Code comment generation, which attempts to summarize the functionality of source code in textual descriptions, plays an important role in automatic software development research. Currently, several structural neural networks have been exploited to preserve the syntax structure of source code based on abstract syntax trees (ASTs). However, they can not well capture both the long‐distance and local relations between nodes while retaining the overall structural information of AST. To mitigate this problem, we present a prototype tool titled PyScribe , which extends the Transformer model to a new encoder‐decoder‐based framework. Particularly, the triplet position is designed and integrated into the node‐level and edge‐level structural features of AST for producing Python code comments automatically. This paper, to the best of our knowledge, makes the first effort to model the edges of AST as an explicit component for improved code representation. By specifying triplet positions for each node and edge, the overall structural information can be well preserved in the learning process. Moreover, the captured node and edge features go through a two‐stage decoding process to yield higher qualified comments. To evaluate the effectiveness of PyScribe , we resort to a large dataset of code‐comment pairs by mining Jupyter Notebooks from GitHub, for which we have made it publicly available to support further studies. The experimental results reveal that PyScribe is indeed effective, outperforming the state‐ofthe‐art by achieving an average BLEU score (i.e., av‐BLEU) of 0.28.
Personalization and Cold Start Problems in Information Retrieval

The retrieval of information within enterprise data stores is consistently challenging due to the dispersion of data across diverse and heterogeneous storage systems, in contrast to the relatively simpler web-based retrieval systems. This paper introduces an enhanced approach to information retrieval, building upon our prior work on collaborative fusion-based retrieval presented in [1]. Our initial solution leveraged the insight that similar users exhibit preferences for comparable search results. It incorporated three key dimensions: user similarity, document similarity, and user-to-document affinity, forming the basis for collaborative information fusion in retrieval processes. The research proposed a methodology involving active feedback for the revision of search results, aiming for highlighted relevance. However, a notable limitation of the previous work was the absence of provisions for personalization and an inability to effectively handle cold start problems. This absence impedes the accurate modeling of user-to-document affinity, thereby impacting the collaborative fusion process. In response, this paper addresses these limitations by refining our collaborative fusion-based information retrieval solution. The enhancements specifically focus on incorporating user personalization and providing a resolution for cold start challenges. Through these improvements, we aim to augment the accuracy and adaptability of our information retrieval system in enterprise settings.
Deep Ensemble Outlier Detection in Lymphography Dataset

Important data instances known as outliers are those whose characteristics differ from those of the majority of the instances in a dataset. With a variety of uses, including fraud detection in credit card transactions and intrusion detection in computer communications, outlier detection is a crucial field of study in statistics and data mining. In the medical field, diseases can also be identified from a variety of laboratory reports using outlier detection techniques. Researchers have developed a number of techniques to identify outliers in healthcare systems. This research aims to identify the outliers using Deep Ensemble Approach in lymphography dataset. In order to identify outliers in the lymphography dataset, this research suggests an ensemble approach based on deep learning and isolation forests. The suggested approach outperforms the individual models, according to experimental results using the lymphography dataset from the UCI machine learning repository that is accessible to the general public.
Data Privacy in Machine Learning

Advancements in machine learning have propelled innovation across various domains, yet the widespread use of large datasets raises significant concerns regarding individual data privacy. This research paper delves into the intricate landscape of "Data Privacy in Machine Learning," elucidating the challenges and potential solutions in preserving the confidentiality of personal information.
Guide to Education and Employment Matching Supply and Demand Project by Ministry of Education and Sophia Technology (SHENZHEN) Co.,Ltd.

Founded on January 5, 2017, Sophia Technology (Shenzhen) Co., Ltd. is an advanced e-commerce, international trade, intelligent transportation, artificial intelligence, aerospace science and technology, software development, digital economy integrated solution service provider. Sophia Technology (Shenzhen) Co., Ltd. participated in the first phase of the Ministry of Education’s supply and demand matching employment and education project. Under the guidance of the Ministry of Education, Sophia Technology (Shenzhen) Co., Ltd. has participated in the Ministry of Education's University-Industry Collaborative Education Program since 2017, and has set up the Ministry of Education’s Industry-University Cooperation Cooperative Education Project with Tsinghua University and other colleges and universities, as well as the Jilin Province Industry-University Cooperative Cooperation Cooperative Education Project of 362 items, and the “Microbial Pharmaceutical Virtual Simulation Teaching System” with Northwestern Polytechnical University has been awarded as the excellent case of the Ministry of Education's Industry-University Cooperation Cooperative Education Program.
Socialization of Prevention Patterns of Wild Racing and Suppressing the Number of Traffic Accidents

The results of this socialization is to provide education to teenagers about the impact of illegal racing activities which are very dangerous for themselves and other drivers. This socialization report can be a reference for creating patterns to prevent illegal racing and traffic accidents. It was found that the role of parents can shape children's personal, commendable attitudes so that they do not violate the law. The aim of the results of this socialization is to educate teenagers about the dangers of illegal racing which can lead to traffic accidents. The method used is community service outreach to communities around areas prone to illegal racing. The results are very useful for evaluating the behavior of teenagers who carry out these negative activities.
Implementation Of The Swin Transformer and Its Application In Image Classification

There are big differences between the field of view of the calculator and the field of natural languages, for example, in the field of vision, the difference is in the size of the object as well as in the accuracy of the pixels in the image, and this contradicts the words in the text, and this makes the adaptation of the transformers to see somewhat difficult.Very recently a vision transformer named Swin Transformer was introduced by the Microsoft research team in Asia to achieve state-of-the-art results for machine translation. The computational complexity is linear and proportional to the size of the input image, because the processing of subjective attention is within each local window separately, and thus results in processor maps that are hierarchical and in deeper layers, and thus serve as the backbone of the calculator's vision in image classification and dense recognition applications. This work focuses on applying the Swin transformer to a demonstrated mathematical example with step-by-step analysis. Additionally, extensive experimental results were carried out on several standardized databases from CIFAR-10, CIFAR-100, and MNIST. Their results showed that the Swin Transformer can achieve flexible memory savings. Test accuracy for CIFAR-10 gave a 71.54% score, while for the CIFAR-100 dataset the accuracy was 46.1%. Similarly, when the Swin transformer was applied to the MNIST dataset, the accuracy increased in comparison with other vision transformer results.
Explaining Data Patterns in Natural Language with Language Models

Large language models (LLMs) have displayed an impressive ability to harness natural language to perform complex tasks. We explore whether we can leverage this ability to find and explain patterns in data. Specifically, given a pre-trained LLM and data examples, we apply interpretable autoprompting (iPrompt) to generate a natural language string explaining the data. iPrompt iteratively generates explanations with an LLM and reranks them based on their performance when used as a prompt. Experiments on a wide range of datasets, from synthetic mathematics to natural language understanding, show that iPrompt can yield meaningful insights by accurately finding dataset explanations that are human-interpretable. Moreover, iPrompt is reasonably efficient, as it does not require access to model gradients and works with relatively small models (e.g. ~6 billion parameters rather than >=100 billion). Finally, experiments with scientific datasets show the potential for iPrompt to aid in scientific discovery.
M1437 at BLP-2023 Task 2: Harnessing Bangla Text for Sentiment Analysis: A Transformer-based Approach

Analyzing public sentiment on social media is helpful in understanding the public’s emotions about any given topic. While numerous studies have been conducted in this field, there has been limited research on Bangla social media data. Team M1437 from George Mason University participated in the Sentiment Analysis shared task of the Bangla Language Processing (BLP) Workshop at EMNLP-2023. The team fine-tuned various BERT-based Transformer architectures to solve the task. This article shows that BanglaBERTlarge, a language model pre-trained on Bangla text, outperformed other BERT-based models. This model achieved an F1 score of 73.15% and top position in the development phase, was further tuned with external training data, and achieved an F1 score of 70.36% in the evaluation phase, securing the fourteenth place on the leaderboard. The F1 score on the test set, when BanglaBERTlarge was trained without external training data, was 71.54%.
Investigating Zero-shot Cross-lingual Language Understanding for Arabic

Numerous languages exhibit shared characteristics, especially in morphological features. For instance, Arabic and Russian both belong to the fusional language category. The question arises: Do such common traits influence language comprehension across diverse linguistic backgrounds? This study explores the possibility of transferring comprehension skills across languages to Arabic in a zero-shot scenario. Specifically, we demonstrate that training language models on other languages can enhance comprehension of Arabic, as evidenced by our evaluations in three key tasks: natural language inference, question answering, and named entity recognition. Our experiments reveal that certain morphologically rich languages (MRLs), such as Russian, display similarities to Arabic when assessed in a zero-shot context, particularly in tasks like question answering and natural language inference. However, this similarity is less pronounced in tasks like named entity recognition.
BLP-2023 Task 2: Sentiment Analysis

We present an overview of the BLP Sentiment Shared Task, organized as part of the inaugural BLP 2023 workshop, co-located with EMNLP 2023. The task is defined as the detection of sentiment in a given piece of social media text. This task attracted interest from 71 participants, among whom 29 and 30 teams submitted systems during the development and evaluation phases, respectively. In total, participants submitted 597 runs. However, only 15 teams submitted system description papers. The range of approaches in the submitted systems spans from classical machine learning models, fine-tuning pre-trained models, to leveraging Large Language Model (LLMs) in zero- and few-shot settings. In this paper, we provide a detailed account of the task setup, including dataset development and evaluation setup. Additionally, we provide a succinct overview of the systems submitted by the participants. All datasets and evaluation scripts from the shared task have been made publicly available for the research community, to foster further research in this domain.
When Your Language Model Cannot Even Do Determiners Right: Probing for Anti-Presuppositions and the Maximize Presupposition! Principle

The increasing interest in probing the linguistic capabilities of large language models (LLMs) has long reached the area of semantics and pragmatics, including the phenomenon of presuppositions. In this study, we investigate a phenomenon that, however, has not yet been investigated, i.e., the phenomenon of anti-presupposition and the principle that accounts for it, the Maximize Presupposition! principle (MP!). Through an experimental investigation using psycholinguistic data and four open-source BERT model variants, we explore how language models handle different anti-presuppositions and whether they apply the MP! principle in their predictions. Further, we examine whether fine-tuning with Natural Language Inference data impacts adherence to the MP! principle. Our findings reveal that LLMs tend to replicate context-based n-grams rather than follow the MP! principle, with fine-tuning not enhancing their adherence. Notably, our results further indicate a striking difficulty of LLMs to correctly predict determiners, in relatively simple linguistic contexts.
Pseudo-Labeling for Domain-Agnostic Bangla Automatic Speech Recognition

Rabindra Nath Nandi, Mehadi Menon, Tareq Muntasir, Sagor Sarker, Quazi Sarwar Muhtaseem, Md. Tariqul Islam, Shammur Chowdhury, Firoj Alam. Proceedings of the First Workshop on Bangla Language Processing (BLP-2023). 2023.
ArTrivia: Harvesting Arabic Wikipedia to Build A New Arabic Question Answering Dataset

We present ArTrivia, a new Arabic question-answering dataset consisting of more than 10,000 question-answer pairs along with relevant passages, covering a wide range of 18 diverse topics in Arabic. We created our dataset using a newly proposed pipeline that leverages diverse structured data sources from Arabic Wikipedia. Moreover, we conducted a comprehensive statistical analysis of ArTrivia and assessed the performance of each component in our pipeline. Additionally, we compared the performance of ArTrivia against the existing TyDi QA dataset using various experimental setups. Our analysis highlights the significance of often overlooked aspects in dataset creation, such as answer normalization, in enhancing the quality of QA datasets. Our evaluation also shows that ArTrivia presents more challenging and out-of-distribution questions to TyDi, raising questions about the feasibility of using ArTrivia as a complementary dataset to TyDi.
CRT-QA: A Dataset of Complex Reasoning Question Answering over Tabular Data

Large language models (LLMs) show powerful reasoning abilities on various text-based tasks. However, their reasoning capability on structured data such as tables has not been systematically explored. In this work, we first establish a comprehensive taxonomy of reasoning and operation types for tabular data analysis. Then, we construct a complex reasoning QA dataset over tabular data, named CRT-QA dataset (Complex Reasoning QA over Tabular data), with the following unique features: (1) it is the first Table QA dataset with multi-step operation and informal reasoning; (2) it contains fine-grained annotations on questions’ directness, composition types of sub-questions, and human reasoning paths which can be used to conduct a thorough investigation on LLMs’ reasoning ability; (3) it contains a collection of unanswerable and indeterminate questions that commonly arise in real-world situations. We further introduce an efficient and effective tool-augmented method, named ARC (Auto-exemplar-guided Reasoning with Code), to use external tools such as Pandas to solve table reasoning tasks without handcrafted demonstrations. The experiment results show that CRT-QA presents a strong challenge for baseline methods and ARC achieves the best result.
How Fragile is Relation Extraction under Entity Replacements?

Yiwei Wang, Bryan Hooi, Fei Wang, Yujun Cai, Yuxuan Liang, Wenxuan Zhou, Jing Tang, Manjuan Duan, Muhao Chen. Proceedings of the 27th Conference on Computational Natural Language Learning (CoNLL). 2023.
Evaluating and Enhancing the Robustness of Code Pre-trained Models through Structure-Aware Adversarial Samples Generation

Code pre-trained models (CodePTMs) have significantly advanced the field of neural code intelligence. Despite their capabilities, these models are susceptible to adversarial attacks that subtly modify the model inputs, resulting in incorrect outputs or predictions. Previous methods of robustness evaluation for CodePTMs primarily stem from a textual perspective, without explicitly taking into account the structure of the code. Furthermore, prior studies fail to encompass a broad enough spectrum of tasks and models. In this paper, we propose a set of novel robustness evaluation methods based on the intrinsic structure of the code. Specifically, we first launch adversarial attacks on crucial identifier tokens and sub-tree structures to explore the impact of imperceptible perturbation. Then, we perform global restructuring of the code using different traversal methods for abstract syntax trees, aiming to explore the model’s sensitivity to input samples with equivalent information. Moreover, for each scenario, we employ adversarial training methods to explore the possibility of restoring the performance of perturbed models. For both code understanding and generation, our proposed method has demonstrated its effectiveness across a wide range of models and tasks, thereby allowing us to make one step forward in our understanding of the inner mechanisms of CodePTMs.
MTGER: Multi-view Temporal Graph Enhanced Temporal Reasoning over Time-Involved Document

The facts and time in the document are intricately intertwined, making temporal reasoning over documents challenging. Previous work models time implicitly, making it difficult to handle such complex relationships. To address this issue, we propose MTGER, a novel Multi-view Temporal Graph Enhanced Reasoning framework for temporal reasoning over time-involved documents. Concretely, MTGER explicitly models the temporal relationships among facts by multi-view temporal graphs. On the one hand, the heterogeneous temporal graphs explicitly model the temporal and discourse relationships among facts; on the other hand, the multi-view mechanism captures both time-focused and fact-focused information, allowing the two views to complement each other through adaptive fusion. To further improve the implicit reasoning capability of the model, we design a self-supervised time-comparing objective. Extensive experimental results demonstrate the effectiveness of our method on the TimeQA and SituatedQA datasets. Furthermore, MTGER gives more consistent answers under question perturbations.