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QuillBot is one of the online programs that can be utilized to enhance students’ writing skills. By integrating the paraphrasing tool QuillBot into the writing instruction, this study aimed to examine the effectiveness of QuillBot paraphraser on the students’ paraphrasing ability. Additionally, this study sought to find out the students' opinions on paraphrasing and on the use of QuillBot in paraphrasing. Adopting a mixed method with explanatory design, this study employed quasi-experimental research with a one group pretest posttest design. There were 44 fourth-semester students taking the Academic Writing course in the English study program at the University of Mataram participating in this study. The students’ paraphrasing ability was tested before and after the treatment with both manual paraphrasing instruction and QuillBot paraphrasing tool. While, the students’ opinions towards paraphrasing instruction integrating the QuillBot paraphrasing tool was discovered through questionnaire. Further, the collected data were analyzed quantitatively and qualitatively to answer the predetermined research objectives. The results of t-test showed that there was a significant difference between the students’ paraphrasing ability before and after the treatment with significance value 0.000 lower than 0.05. In addition, most students responded positively to the integration of QuillBot in paraphrasing instruction, as it not only improved their literacy skills but also their motivation in learning paraphrasing in which the use of QuillBot paraphraser is simple, easy, instant and time-saving. This study highlights and confirms the potential of AI tools like QuillBot to enhance language instruction. Other reseachers might compare the efficacy of QuillBot paraphraser to other automatic paraphraser in writing instruction and use both questionnaire and interview to obtain more detailed information about the students’ attitudes or opinions.

Abstract The present paper provides an overview of an online module for formative assessment of summary writing skills for second language (L2) introductory academic writing instruction in Japan and presents initial empirical results on how Japanese undergraduate students’ summary writing performance changed with a series of automated summary content feedback delivered in the module. A key feature of this module was the provision of fine-grained feedback delivered as scaffolding during revisions in terms of two key aspects of summary content: main idea representation and paraphrasing. Participants were 64 Japanese undergraduate engineering majors in introductory academic writing courses at a private university in Tokyo. The students completed two summary writing tasks provided through the online module. Results of a multivariate analysis of variance showed significant improvement of the content analytic score on revision on the initial summary task, and that this improved performance level was retained on a transfer task. The language use analytic score also improved significantly on the transfer task. Detailed analyses of learner-produced summaries based on descriptive statistics further suggested that the learners made substantively meaningful changes concerning main idea coverage and verbatim copying of the source text while still meeting the length requirement, although the results differed somewhat across the source texts assigned. Despite some study limitations, these results provide initial support for immediate content feedback provision for the development of basic summary writing skills.

The main thrust of the study was to explore the third-year English student's sentiments on the application of the artificial intelligence (AI)-integrated paraphrasing tools. Specifically, it aimed to investigate the benefits and drawbacks brought by AI paraphrasing tools towards the writing skills of the students. The investigation utilized a semi-structured interviews with an openended questionnaire using an audio-video recorder. The data gathered were interpreted using the Thematic Analysis of Braun and Clarke. The study was carried out at one of the campuses of a state university located at Candijay, Bohol, Philippines. Using the Purposive sampling technique, twelve (12) respondents provided information on the research endeavor. The findings revealed that students had a positive opinion of AI-integrated paraphrase tools: they saw them as helpful resources that significantly improve their academic writing process; it includes plagiarism reduction, efficiency, and timesaving, and aids in rephrasing text. The findings also revealed the risks and issues of using AI-integrated paraphrasing tools, such as Prone to plagiarism, automated suggestions dependency, and loss of original meaning and context. With that, the students showed how they deal with those risks and issues, including as responsible users and thorough editing and reviewing. In accordance with the study, students are encouraged not to rely excessively on AI-integrated paraphrase tools, even though they can improve their writing abilities. This research emphasizes that students play a pivotal role in ensuring the appropriateness of texts generated by AI-integrated paraphrasing tools by mastering the art of proper paraphrasing. [ABSTRACT FROM AUTHOR]

The present paper provides an overview of an online module for formative assessment of summary writing skills for second language (L2) introductory academic writing instruction in Japan and presents initial empirical results on how Japanese undergraduate students' summary writing performance changed with a series of automated summary content feedback delivered in the module. A key feature of this module was the provision of fine-grained feedback delivered as scaffolding during revisions in terms of two key aspects of summary content: main idea representation and paraphrasing. Participants were 64 Japanese undergraduate engineering majors in introductory academic writing courses at a private university in Tokyo. The students completed two summary writing tasks provided through the online module. Results of a multivariate analysis of variance showed significant improvement of the content analytic score on revision on the initial summary task, and that this improved performance level was retained on a transfer task. The language use analytic score also improved significantly on the transfer task. Detailed analyses of learner-produced summaries based on descriptive statistics further suggested that the learners made substantively meaningful changes concerning main idea coverage and verbatim copying of the source text while still meeting the length requirement, although the results differed somewhat across the source texts assigned. Despite some study limitations, these results provide initial support for immediate content feedback provision for the development of basic summary writing skills. [ABSTRACT FROM AUTHOR]

This article offers a case study of using a flipped, synchronous virtual workshop to introduce first-year writing students to the “Scholarship as Conversation” frame of the ACRL Framework for Information Literacy for Higher Education. Before the workshop, students completed an asynchronous Preventing Plagiarism Tutorial that introduced them to paraphrasing and citations to develop a foundation for the workshop discussion. In the workshop, librarians used the Cephalonian method to introduce students to “Scholarship as Conversation.” To provide a realworld example of scholarly discourse, librarians cotaught the workshop with a faculty member who self-identifies as a Filipina American and whose research specialty is in Asian American cultural studies, which coincided with the course theme for the week. Workshop evaluations showed that students responded favorably to the presentation style. Learners expressed an awareness of the “Scholarship as Conversation” concept and the role that citation plays.

We examined how a group of Chinese undergraduate students understood paraphrasing and source use conventions for research paper writing in English. Prior scholarship has generated valuable insights about novice second language (L2) writers' unconventional source use practices, but little is known about how these writers interpret and understand source use expectations for English academic writing. We followed a group of nine Chinese undergraduate students for an academic term as they learned to paraphrase and write with sources for their research paper assignments. Drafts of students' papers were collected and rounds of text-based interviews were conducted where students were asked to explain their source use decisions. Findings showed that the students actively and constantly referenced their source use knowledge of Chinese essay writing to help make sense of source use expectations for English academic writing, although their understanding of English academic source use as well as their rendition of culture may likely be viewed by expert academic writers as 'insufficient' or 'inadequate'. We conclude by highlighting the importance of foregrounding learner voice and acknowledging the legitimacy of the interpretations of novice L2 writers as intercultural informants in the teaching of English academic source use. [ABSTRACT FROM AUTHOR]

The relationship between paraphrase and plagiarism is complex and has been the subject of multiple research studies. However, recent advances in Generative AI (GenAI) have disrupted the standard practices of academic writing and established a renewed focus on how learners acquire skills in English writing in a higher education context. To understand the future of academic writing in the technological era, further investigation of how and why students choose to paraphrase and engage with source material is needed to guide best practices in instruction, policy, and research. This exploratory study seeks to fill this gap and provide insight into the choices English as a Second Language (ESL) student writers make in an English for Academic Purposes (EAP) context. Using a text-based interview method and custom-designed academic writing task, we examine how students engage with and make decisions about using source material. Results reveal that participants focus more on sentence-level approaches to paraphrasing and mechanistic methods of language transformation. This is partly motivated by prior learning experiences but may also be related to adopting a risk-averse approach to language use to avoid accusations of plagiarism. Furthermore, our participants expressed a low level of confidence in the ability of new technological tools to help learners acquire skills in English writing. These findings suggest that there is a need for further research exploring policies to enable experimentation in EAP programs, as well as exploring acceptance and belief in new technologies' ability to support paraphrasing and source use. [ABSTRACT FROM AUTHOR]

This research aims to examine how higher education students paraphrase their academic writing to reduce high Turnitin similarity scores. The research uses a descriptive-qualitative research design. Data were collected through observations and in-depth interviews. The observation was conducted by analyzing 20 higher education students' theses in terms of paraphrasing practices. The in-depth interviews with five selected participants were conducted by asking some questions related to the observation results about paraphrasing practices. Based on the observation and the in-depth interviews, the researcher found that most of the students prefer to do paraphrasing practices online compared to paraphrasing practices manually. In addition, most students already know the meaning of paraphrasing but some students have not applied it adequately. The researchers conclude that online paraphrasing is a great idea that can be used to simplify paraphrasing activities. However, the students need to re-read the results of the paraphrase from the online paraphrase to be analyzed and revised if needed. It is crucial to focus on the overall quality of the paraphrased results, making sure to check for proper word choice and grammar. In addition, further research is required to determine the extent to which the text's original context and meaning have been successfully maintained. [ABSTRACT FROM AUTHOR]

ABSTRACTThe class imbalance problem, in which the distribution of different classes in training data is unequal or skewed, is a prevailing problem. This can lead to classifier algorithms being biased, negatively impacting the performance of the minority class. In this paper, we addressed the class imbalance problem in datasets for aspect-based sentiment classification. Aspect-based Sentiment Classification (AbSC) is a type of fine-grained sentiment analysis in which sentiments about particular aspects of an entity are extracted. In this work, we addressed the issue of class imbalance by creating synthetic data. For synthetic data generation, two techniques have been proposed: paraphrasing using the PEGASUS fine-tuned model and backtranslation using the M2M100 neural machine translation model. We compared these techniques with two other class balancing techniques, such as weighted oversampling and cross-entropy loss with class weight. An extensive experimental study has been conducted on three benchmark datasets for restaurant reviews: SemEval-2014, SemEval-2015, and SemEval-2016. We applied these methods to the BERT-based deep learning model for aspect-based sentiment classification and studied the effect of balancing the data on the performance of these models. Our proposed balancing technique, using synthetic data, yielded better results than the other two existing methods for dealing with multi-class imbalance.

The class imbalance problem, in which the distribution of different classes in training data is unequal or skewed, is a prevailing problem. This can lead to classifier algorithms being biased, negatively impacting the performance of the minority class. In this paper, we addressed the class imbalance problem in datasets for aspect-based sentiment classification. Aspect-based Sentiment Classification (AbSC) is a type of fine-grained sentiment analysis in which sentiments about particular aspects of an entity are extracted. In this work, we addressed the issue of class imbalance by creating synthetic data. For synthetic data generation, two techniques have been proposed: paraphrasing using the PEGASUS fine-tuned model and backtranslation using the M2M100 neural machine translation model. We compared these techniques with two other class balancing techniques, such as weighted oversampling and cross-entropy loss with class weight. An extensive experimental study has been conducted on three benchmark datasets for restaurant reviews: SemEval-2014, SemEval-2015, and SemEval-2016. We applied these methods to the BERT-based deep learning model for aspect-based sentiment classification and studied the effect of balancing the data on the performance of these models. Our proposed balancing technique, using synthetic data, yielded better results than the other two existing methods for dealing with multi-class imbalance. [ABSTRACT FROM AUTHOR]

Frequently Asked Questions (FAQs) systems rely on semantic symmetry similarity measuring between two sentences. To address the challenges of insufficient training data and limited domain-specific understanding in low-resource FAQs, this paper proposes a general framework, PDAM-FAQ, to solve these issues. Firstly, we propose a paraphrasing-based data augmentation model that integrates syntactic information and edit vectors. Using a rule-based approach, it retrieves template sentences from a corpus and masks relevant words with special characters. Edit vectors between the original and paraphrase sentences are added to a pre-trained model’s encoding layer to enhance the model’s ability to learn the differences between the original and reference paraphrase sentences. Additionally, this paper presents a mixed-feature semantic matching model based on SimBERT. The model extracts keyword features from the text, replacing these keywords with special characters to construct intent features. These intent features, along with the user question and keyword features, are then concatenated to form the model’s input. Experiments were conducted respectively on the paraphrasing-based data augmentation model, mixed-feature semantic matching model and their comprehensive application in a low-resource domain-specific FAQ. The experimental results show that the proposed framework effectively improve the performance of domain-specific FAQ system.

In this paper, we describe data preparation for our proposed chatbot PerInfEx (Persian Information Extraction chatbot). It aims to interactively chit-chat with users in Persian and by asking the least number of direct questions, extract as much personal information as possible such as user’s age or occupation. Collecting data in considerable size and aligned with our system’s specifics is a crucial step to train data-hungry modules of Natural Language Understating (NLU) and Natural Language Generating (NLG). Initially, for NLU module, we collect 99 free-discussion dialogues and crawl 74 English training conversations as more-general datasets while also manually translate 72 dialogues of ConvAI2 corpus. Moreover, we gamify collection by implementing a chatting website results in 94 dialogues. It detects direct questions and assigns random profiles to participants. They should guess the opponents profile. Also, we propose two augmentation methods: a semi-automatic and a novel fully automatic method, comprehensively evaluated on NLU benchmarks and applied on our datasets. Also, by prompting OpenAI’s GPT-3.5 model, we automatically generate 304 dialogues. The first part of these datasets is manually annotated while we use an active learning method for annotating rest of them. Next, to evaluate data quality, we assess them extrinsically using NLU baseline which results in intent-accuracy = 88.64, slot-F1 = 83.68 and exact-match = 78.22. Also, for NLG module, we automatically translate almost the rest of ConvAI2 corpus (16,217 dialogues) and paraphrase previously sets for its fine-tuning using GPT-3.5 model. Their assessment using our NLG baseline results in perplexity of 15.74 on train and 52.17 on test set.

This study investigates the problems faced by EFL students and their coping strategies to deal with problems when paraphrasing journal articles. To achieve the study objectives, the researchers utilized a narrative inquiry design with two semi-structured interviews. The gathered data were then analyzed by using thematic analysis. Four EFL students participated in this study and shared their experiences regarding the problems they faced when paraphrasing journal articles. The findings presented that the four students perceived limited vocabulary, lack of awareness regarding the criteria for effective paraphrasing and the proper stages of paraphrasing, struggle to find the appropriate synonyms, and lack of grammar mastery. Furthermore, it was discovered that there were several strategies adopted by the students to overcome the problem of paraphrasing journal articles such as changing active to passive voice, eliminating unnecessary words, utilizing paraphrasing tools, and consulting with others. This research can be a form of self-reflection for the readers, especially students, to be more aware of the importance of mastering paraphrasing skills.

Many students who study English as a foreign language (EFL) often find it challenging to paraphrase while writing from source texts. Lacking such an ability can lead to different meanings as well as copying another person’s ideas, words or work. However, little research has been done to integrate tool consultation to assist students in paraphrasing. To address this gap, this study explored whether guided tool consultation with paraphrasing strategy instruction can help students improve their overall paraphrasing performance. During an 18-week course, a class of students were trained to use three different e-tools to find synonyms: Microsoft Word thesaurus, Oxford Living Dictionaries synonyms (now called Thesaurus.com), and Linggle. Adopting a mixed-method approach, data sets included: pre-posttest drafts (summary writing), surveys, screen recordings, and interviews. The results showed significant differences between the pre- and post-tests. The majority of the lexical and phrasal paraphrases was suitable, while only a few were inaccurate. The students demonstrated the ability to consult the tools for changing synonyms and were able to apply taught strategies to restructure and restate the original sentences. Although students revealed different perceptions of the usefulness of the three tools for finding synonyms, they generally agreed that paraphrasing strategies combined with tool training were beneficial for learning. Pedagogical implications and research suggestions are provided based on the findings.

The present study aims to investigate students’ learning strategies and barriers encountered in extensive reading activities. This research was designed with a qualitative case study method. The study recruited six Indonesian university EFL students majoring in English language education to participate and a lecturer in the study program. Data were garnered through semi-structured interviews as a data collection method. The data were analyzed thematically to find emerging themes. The research findings demonstrate that the EFL students employed three extensive reading comprehension strategies. The three strategies included predicting by inferring the meaning of unfamiliar words by making guesses based on the context, guessing from context by utilizing previous knowledge about the topics in the text, and paraphrasing by creating sentences from the obtained information. Another finding also uncovers the university students’ barriers in extensive reading, including the students’ language background and lack of interest in reading. This study concludes with practical implications for how EFL students engage in extensive reading with various learning strategies and how university teachers boost their students’ interests in more diverse extensive reading activities.

As the first “Union Version” in the history of Protestant Chinese biblical translation, the Delegates’ Version (hereafter DV) was rendered in the elegant style of classical Chinese and became a paradigm in this field. Focusing on “paraphrasing” as their translation strategy applied to this version, the present article, through the use of historical materials, traces it back to its origins and comments on the controversies aroused by this strategy in contemporaneity and later generations. Implanted with various key terms from Chinese classics, especially Confucian classics, this version carries on an intertextual dialogue between the Bible and Chinese traditional cultures, which can be demonstrated in some cases concerning ethical terms. In addition, through comparison between the DV and other Chinese biblical versions published in the late Qing dynasty, we discover various renditions in the DV were also adopted by later versions, manifesting the advantage of classical Chinese in rendering the Bible and thus its far-reaching influence. In summary, when evaluating the DV, it is reasonable to distinguish between paraphrasing and wrong translation: considerable paraphrases are “functional equivalences” while some are purposely inter-cultural interpretations, which are examined closely in the present article. [ABSTRACT FROM AUTHOR]

Teachers play a key role in communicating with parents to support student learning. One of the more challenging aspects of this role for teachers is having a difficult conversation with a parent about their child. Difficult conversations, when needed, are best accomplished face-to-face incorporating effective communication skills to promote a positive and productive interaction. This article reviews a number of key practical communication skills that teachers can use when communicating with parents including use of clear vocabulary, active listening, I-messages, questioning strategies, paraphrasing and summarizing, leveled information, as well as attention to nonverbal messages. Strategies to support both planned and unplanned difficult conversations are discussed. Barriers to having difficult conversations between teachers and parents are outlined as well as new directions for research. Effective communication skills are integral for teachers to hold difficult, yet productive, conversations with parents to promote partnerships and to support student success. [ABSTRACT FROM AUTHOR]

In this paper, we study natural language paraphrasing from both corpus creation and modeling points of view. We focus in particular on the methodology that allows the extraction of challenging examples of paraphrase pairs in their natural textual context, leading to a dataset potentially more suitable for evaluating the models' ability to represent meaning, especially in document context, when compared with those gathered using various sentence-level heuristics. To this end, we introduce the Turku Paraphrase Corpus, the first large-scale, fully manually annotated corpus of paraphrases in Finnish. The corpus contains 104,645 manually labeled paraphrase pairs, of which 98% are verified to be true paraphrases, either universally or within their present context. In order to control the diversity of the paraphrase pairs and avoid certain biases easily introduced in automatic candidate extraction, the paraphrases are manually collected from different paraphrase-rich text sources. This allows us to create a challenging dataset including longer and more lexically diverse paraphrases than can be expected from those collected through heuristics. In addition to quality, this also allows us to keep the original document context for each pair, making it possible to study paraphrasing in context. To our knowledge, this is the first paraphrase corpus which provides the original document context for the annotated pairs. We also study several paraphrase models trained and evaluated on the new data. Our initial paraphrase classification experiments indicate a challenging nature of the dataset when classifying using the detailed labeling scheme used in the corpus annotation, the accuracy substantially lacking behind human performance. However, when evaluating the models on a large scale paraphrase retrieval task on almost 400M candidate sentences, the results are highly encouraging, 29–53% of the pairs being ranked in the top 10 depending on the paraphrase type. The Turku Paraphrase Corpus is available at github.com/TurkuNLP/Turku-paraphrase-corpus as well as through the popular HuggingFace datasets under the CC-BY-SA license. [ABSTRACT FROM AUTHOR]

Plagiarism is a significant issue, especially among students, in the education field, where the internet has made a vast amount of information easily accessible. Although numerous plagiarism detection tools are available for English text, detecting plagiarism in low-resource languages such as Hindi remains a challenge. This study proposes a novel case-based approach to evaluate the effectiveness of various plagiarism detection tools on Hindi documents. The proposed approach maps similarities between Hindi documents at the word, phrase, and sentence levels, and generates diverse cases using stopword removal and stemming pre-processing techniques. The study calculates the similarity score between the original Hindi document and its paraphrased version at the word, sentence, and document levels. The results show that the proposed approach outperforms existing plagiarism detection tools for Hindi text, indicating its potential to enhance academic integrity and prevent plagiarism. [ABSTRACT FROM AUTHOR]

Background: The introduction of ChatGPT by OpenAI has garnered significant attention. Among its capabilities, paraphrasing stands out. Objective: This study aims to investigate the satisfactory levels of plagiarism in the paraphrased text produced by this chatbot. Methods: Three texts of varying lengths were presented to ChatGPT. ChatGPT was then instructed to paraphrase the provided texts using five different prompts. In the subsequent stage of the study, the texts were divided into separate paragraphs, and ChatGPT was requested to paraphrase each paragraph individually. Lastly, in the third stage, ChatGPT was asked to paraphrase the texts it had previously generated. Results: The average plagiarism rate in the texts generated by ChatGPT was 45% (SD 10%). ChatGPT exhibited a substantial reduction in plagiarism for the provided texts (mean difference −0.51, 95% CI −0.54 to −0.48; P <.001). Furthermore, when comparing the second attempt with the initial attempt, a significant decrease in the plagiarism rate was observed (mean difference −0.06, 95% CI −0.08 to −0.03; P <.001). The number of paragraphs in the texts demonstrated a noteworthy association with the percentage of plagiarism, with texts consisting of a single paragraph exhibiting the lowest plagiarism rate (P <.001). Conclusion: Although ChatGPT demonstrates a notable reduction of plagiarism within texts, the existing levels of plagiarism remain relatively high. This underscores a crucial caution for researchers when incorporating this chatbot into their work. [ABSTRACT FROM AUTHOR]

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The current study investigated the effects of a behavior skills training on listening behaviors of adults in romantic relationships. Participants' partners selected desired criteria ranges for six potential behaviors to target through pretest surveys, video questionnaires, and verbal report. The researcher then selected three behaviors to target (e.g., minimal encouragers, supportive statements, and paraphrasing) and used a behavior skills training to train those behaviors to the desired amount. The results showed that the training was effective for four participant couples in increasing effective listening behaviors in their relationships. Participants' performances generally maintained at criterion during confederate probes and partner probes after the skills training with the researcher. [ABSTRACT FROM AUTHOR]

Text classification is an issue of high priority in text mining, information retrieval that needs to address the problem of capturing the semantic information of the text. However, several approaches are used to detect the similarity in short sentences, most of these miss the semantic information. This paper introduces a hybrid framework to classify semantically similar short texts from a given set of documents. A real-life dataset – Quora Question Pairs is used for this purpose. In the proposed framework, the question pairs of short texts are pre-processed to eliminate junk information and 25 tokens, and string-equivalence features are engineered from the dataset, which plays a major role in classification. The redundant and overlapping features are removed and word vectors are created by using TF-IDF weighted average FastText approach. A 623-dimensional data model is obtained combining all the obtained features, and the same is then fed to the Light Gradient Boosting Machine for classification. At last, the hyperparameters are tuned to attain optimized log_loss. The experimental results show that the proposed framework can achieve 81.47% accuracy which is at par with the other state-of-art models. [ABSTRACT FROM AUTHOR]

This study attempts to explore the reflection of the connotative meanings in the translation of the Arabic lexical items qalil (few or little) and kathir (much or many) and their derivations from Arabic into English and Spanish. In order to conduct the study, some Qur’anic verses containing the two items are chosen, and two translations of the verses, English and Spanish, are selected to evaluate the portrayal of the connotative meanings of the two items in the translation. The study shows that the verses containing qalil and its derivations hold positive and favorable connotations and overtones, while the verses containing kathir and its derivations hold negative ones. It shows that the translation challenge of the antonymous lexical items qalil and kathir does not lie in their denotative meaning, but rather in the connotative one. It further reveals that lexical items can be concurrently antonymous at the denotative and connotative levels, a semantic fact that should be given enough attention in translation. The study concludes that since literal translation does not convey the various implications of lexical items, exegetic translation is suggested as an appropriate translation strategy to transfer these implications. [ABSTRACT FROM AUTHOR]

While the typology of paraphrasing revolves around linguistic changes of paraphrasing, little is known about the importance of different types of linguistic changes and their relationship between paraphrasing performance and L2 proficiency. Empirical enquiry has focused on L2 writers' inappropriate paraphrasing performance against the norm of L1 writer, which is problematic in that L2 and L1 writers displayed considerable variation in paraphrasing. The present study drew upon 202 Chinese EFL writers' written responses in a paraphrasing test to look into the discrete linguistic transformations in paraphrasing and examine how the frequency of different linguistic changes in paraphrasing relates to their paraphrasing performance and L2 proficiency. Correlation analysis was run to analyze the relationship between the frequency of linguistic changes and paraphrasing performance. Multivariate analysis of variance analysis was conducted to examine how the frequency of linguistic changes relates to L2 proficiency. The findings revealed that Conceptual Transformation had the highest significant correlation with paraphrasing scores, followed by Lexical Transformation and then Syntactic Transformation. The frequency of Synonym Substitution, Morphology, Multiple Word Units, Phrase/Clause Shift, Active/Passive Shift and Conceptual Transformation increased as L2 writers' proficiency levels increased. Implications are drawn from the findings for paraphrasing instruction and assessment, research in paraphrasing and L2 writers' academic writing practice. [ABSTRACT FROM AUTHOR]

Humans are naturally endowed with the ability to write in a particular style. They can, for instance, rephrase a formal letter in an informal way, convey a literal message with the use of figures of speech or edit a novel by mimicking the style of some well-known authors. Automating this form of creativity constitutes the goal of style transfer. As a natural language generation task, style transfer aims at rewriting existing texts, and specifically, it creates paraphrases that exhibit some desired stylistic attributes. From a practical perspective, it envisions beneficial applications, like chatbots that modulate their communicative style to appear empathetic, or systems that automatically simplify technical articles for a non-expert audience. Several style-aware paraphrasing methods have attempted to tackle style transfer. A handful of surveys give a methodological overview of the field, but they do not support researchers to focus on specific styles. With this paper, we aim at providing a comprehensive discussion of the styles that have received attention in the transfer task. We organize them in a hierarchy, highlighting the challenges for the definition of each of them and pointing out gaps in the current research landscape. The hierarchy comprises two main groups. One encompasses styles that people modulate arbitrarily, along the lines of registers and genres. The other group corresponds to unintentionally expressed styles, due to an author's personal characteristics. Hence, our review shows how these groups relate to one another and where specific styles, including some that have not yet been explored, belong in the hierarchy. Moreover, we summarize the methods employed for different stylistic families, hinting researchers towards those that would be the most fitting for future research. [ABSTRACT FROM AUTHOR]

Undoubtedly, the world of childhood is one of the most important periods in the life of every human being, a period during which everyone's personality is formed. Therefore, education and intellectual products offered to the child in this period are significant. Animation is a tool affecting the thinking of this age group. Among the humor works that have been done in this field in Iran is the "Shekarestan" animation TV series (2008). It is rooted in old Iranian-Eastern stories, proverbs, fables, and folk tales. This animation TV series has been dubbed into other languages including Arabic. The humor used in this series has special subtleties, which require a lot of care and attention to translate into Arabic. This paper seeks to criticize and analyze the humor translation methods used in the story "False Brother" of this series via a descriptive-analytical method based on the humor translation approach of Magdalena Panek. The result shows that the literal translation method, which is the most profitable humor translation method from the author's point of view, has a high frequency in this part of the series, and this issue has greatly reduced the artistic value of the dubbing group. However, for their efforts, this group has used other strategies of Panek’s techniques, such as modulation, paraphrase, condensation, substitution, deletion, neologisms, and transposition. The general result of this research is that the Arabic dubbing team has not been very successful in localizing this part of Shekarestan in Arabic culture and the humor used in the original language of this story has been sacrificed to the Arabic dubbing.

One crucial skill to master by university students related to academic writing activity is paraphrasing skill. However, in Indonesian context, the university students still find paraphrasing challenging. Therefore, this study is intended to explore the use of read, reread, list, compose (RRLC) strategy to enhace the students’ paraphrasing skill in a college context. This study utilizes a qualitative case study design where the data were obtained through observation. The study revealed that RRLC stages can be implemented in learning activity. It engaged the students in activating their prior knowledge, doing an independent reading, discussing, and sharing their understanding about the text before paraphrasing. It assisted them to have better comprehension of the text to be paraphrased. In addition, during the implementation of composing stage in guided practice, the students’ paraphrased works indicated that the students were able to create sentences in different structures—active or passive constructions, use the synonyms, and retain the original meaning. These three skills helped them reduce the similiarity of their paraphrased works from the original version. This study recommends that RRLC strategy be implemented in teaching paraphrasing at college or lower level with some adjustments to meet the students’ basic English skill. [ABSTRACT FROM AUTHOR]

The Text REtrieval Conference Conversational assistance track (CAsT) is an annual conversational passage retrieval challenge to create a large-scale open-domain conversational search benchmarking. However, as of yet, the datasets used are small, with just more than 1,000 turns and 100 conversation topics. In the first part of this research, we address the dataset limitation by building a much larger novel multi-turn conversation dataset for conversation search benchmarking called Expanded-CAsT (ECAsT). ECAsT is built using a multi-stage solution that uses a combination of conversational query reformulation and neural paraphrasing and also includes a new model to create multi-turn paraphrases. The meaning and diversity of paraphrases are evaluated with human and automatic evaluation. Using this methodology, we produce and release to the research community a conversational search dataset that is 665% more extensive in terms of size and language diversity than is available at the time of this study, with more than 9,200 turns. The augmented dataset not only provides more data but also more language diversity to improve conversational search neural model training and testing. In the second part of the research, we use ECAsT to assess the robustness of traditional metrics for conversational evaluation used in CAsT and identify its bias toward language diversity. Results show the benefits of adding language diversity for improving the collection of pooled passages and reducing evaluation bias. We found that introducing language diversity via paraphrases returned up to 24% new passages compared to only 2% using CAsT baseline.