Reviewerly/exHarmony huggingface dataset:
Visually Grounded Follow-up Questions: a Dataset of Spatial Questions Which Require Dialogue History

In this paper, we define and evaluate a methodology for extracting history-dependent spatial questions from visual dialogues.We say that a question is history-dependent if it requires (parts of) its dialogue history to be interpreted.We argue that some kinds of visual questions define a context upon which a follow-up spatial question relies.We call the question that restricts the context: trigger, and we call the spatial question that requires the trigger question to be answered: zoomer.We automatically extract different trigger and zoomer pairs based on the visual property that the questions rely on (e.g.color, number).We manually annotate the automatically extracted trigger and zoomer pairs to verify which zoomers require their trigger.We implement a simple baseline architecture based on a SOTA multimodal encoder.Our results reveal that there is much room for improvement for answering historydependent questions. QuestionerOracle 1. is it a car?no 2. is it white color?no 3. is it red?no 4. black?yes 5. are there 2 black cars?yes 6. 1 that is in the left?no 1. is it a car?no 2. is it something on a building?no 3. is it some light?no 4. is it the street light?no 5. can you see 5 or 6 of them on the right side? no 6.ok..so it is a light but may be the red lamps?yes 7. is it the 1st one from right?yes
Improved English to Hindi Multimodal Neural Machine Translation

Machine translation performs automatic translation from one natural language to another. Neural machine translation attains a state-of-the-art approach in machine translation, but it requires adequate training data, which is a severe problem for low-resource language pairs translation. The concept of multimodal is introduced in neural machine translation (NMT) by merging textual features with visual features to improve low-resource pair translation. WAT2021 (Workshop on Asian Translation 2021) organizes a shared task of multimodal translation for English to Hindi. We have participated the same with team name CNLP-NITS-PP in two submissions: multimodal and text-only NMT. This work investigates phrase pairs injection via data augmentation approach and attains improvement over our previous work at WAT2020 on the same task in both text-only and multimodal NMT. We have achieved second rank on the challenge test set for English to Hindi multimodal translation where Bilingual Evaluation Understudy (BLEU) score of 39.28, Rank-based Intuitive Bilingual Evaluation Score (RIBES) 0.792097, and Adequacy-Fluency Metrics (AMFM) score 0.830230 respectively.
How Low is Too Low? A Computational Perspective on Extremely Low-Resource Languages

Despite the recent advancements of attentionbased deep learning architectures across a majority of Natural Language Processing tasks, their application remains limited in a lowresource setting because of a lack of pretrained models for such languages.In this study, we make the first attempt to investigate the challenges of adapting these techniques to an extremely low-resource language -Sumerian cuneiform -one of the world's oldest written language attested from at least the beginning of the 3rd millennium BC.Specifically, we introduce the first cross-lingual information extraction pipeline for Sumerian, which includes part-of-speech tagging, named entity recognition, and machine translation.We introduce InterpretLR, an interpretability toolkit for low-resource NLP and use it alongside human evaluations to gauge the trained models.Notably, all our techniques and most components of our pipeline can be generalised to any low-resource language.We publicly release all our implementations including a novel data set with domain-specific pre-processing to promote further research in this domain.
A Survey of Feature Extraction for Pap-Smear Image Classification

Pap smear is a screening procedure for the cervical cancer. It is used to detect precancerous and cancerous in the cervix. High grade changes can be detected through a Pap smear test and the treatment can prevent the growth of cancer. This paper describes many features of single-cell images that is used to extract relevant and informative data. The features were used for the cervical cancer classification and their limitations are also discussed in detail.
Gamification in economics and its impact on students’ achievement: Lesson from covid-19 in Indonesia

Providing teaching and learning activities during the Covid-19 pandemic has been a global challenge, and the government has responded to this issue by conducting distance learning using technology. This study introduces and evaluates gamification learning in economics employing to enhance the students’ achievements. This study elaborates on a new learning model, “e-crowdwar mobile game,” that can be used for distance learning activities. This research adopted classroom action research to gain an understanding of the implementation of this new model for students’ achievement. The work involved senior high school students in several regions in East Java of Indonesia. Accordingly, the data were analyzed using both quantitative and qualitative approaches. The findings indicate that the implementation of gamification learning faces some difficulties in both teachers and students. However, this new model outperforms the conventional learning model with the teacher-centered approach, and it can be considered an alternative learning model for distance learning using technology.&#x0D; &#x0D; Keywords: E-crowdwar, gamification learning, mobile-Learning, students’ achievements
Ensemble ALBERT and RoBERTa for Span Prediction in Question Answering

Retrieving relevant answers from heterogeneous data formats, for given for questions, is a challenging problem.The process of pinpointing relevant information suitable to answer a question is further compounded in large document collections containing documents of substantial length.This paper presents the models designed as part of our submission to the DialDoc21 Shared Task (Documentgrounded Dialogue and Conversational Question Answering) for span prediction in question answering.The proposed models leverage the superior predictive power of pretrained transformer models like RoBERTa, ALBERT and ELECTRA, to identify the most relevant information in an associated passage for the next agent turn.To further enhance the performance, the models were fine-tuned on different span selection based question answering datasets like SQuAD2.0 and Natural Questions (NQ) corpus.We also explored ensemble techniques for combining multiple models to achieve enhanced performance for the task.Our team SB NITK ranked 6 th on the leaderboard for the Knowledge Identification task, and our best ensemble model achieved an Exact score of 58.58 and an F1 score of 73.39.
Natural Language Adversarial Defense through Synonym Encoding

In the area of natural language processing, deep learning models are recently known to be vulnerable to various types of adversarial perturbations, but relatively few works are done on the defense side. Especially, there exists few effective defense method against the successful synonym substitution based attacks that preserve the syntactic structure and semantic information of the original text while fooling the deep learning models. We contribute in this direction and propose a novel adversarial defense method called Synonym Encoding Method (SEM). Specifically, SEM inserts an encoder before the input layer of the target model to map each cluster of synonyms to a unique encoding and trains the model to eliminate possible adversarial perturbations without modifying the network architecture or adding extra data. Extensive experiments demonstrate that SEM can effectively defend the current synonym substitution based attacks and block the transferability of adversarial examples. SEM is also easy and efficient to scale to large models and big datasets.
YoungSheldon at SemEval-2021 Task 7: Fine-tuning Is All You Need

In this paper, we describe our system used for SemEval 2021 Task 7: HaHackathon: Detecting and Rating Humor and Offense.We used a simple fine-tuning approach using different Pre-trained Language Models (PLMs) to evaluate their performance for humor and offense detection.For regression tasks, we averaged the scores of different models leading to better performance than the original models.We participated in all SubTasks.Our best performing system was ranked 4 in SubTask 1-b, 8 in SubTask 1-c, 12 in SubTask 2, and performed well in SubTask 1-a.We further show comprehensive results using different pre-trained language models which will help as baselines for future work.
cs60075_team2 at SemEval-2021 Task 1 : Lexical Complexity Prediction using Transformer-based Language Models pre-trained on various text corpora

This paper describes the performance of the team cs60075 team2 at SemEval 2021 Task 1 -Lexical Complexity Prediction.The main contribution of this paper is to finetune transformer-based language models pretrained on several text corpora, some being general (E.g., Wikipedia, BooksCorpus), some being the corpora from which the CompLex Dataset was extracted, and others being from other specific domains such as Finance, Law, etc.We perform ablation studies on selecting the transformer models and how their individual complexity scores are aggregated to get the resulting complexity scores.Our method 1 achieves a best Pearson Correlation of 0.784 in sub-task 1 (single word) and 0.836 in sub-task 2 (multiple word expressions).
Assessment of eye-tracking scanpath outliers using fractal geometry

<h2>Abstract</h2> Outlier scanpaths identification is a crucial preliminary step in designing visual software, digital media analysis, radiology training and clustering participants in eye-tracking experiments. However, the task is challenging due to the visual irregularity of the scanpath shapes and the difficulty in dimensionality reduction due to geometric complexity. Conventional approaches have used heat maps to exclude scanpaths that lack a similarity pattern. However, the typically-used packages, such as ScanMatch and MultiMatch often generate discordant results when outlier identification is done empirically. This paper introduces a novel outlier evaluation approach by integrating the fractal dimension (FD), capturing the geometrical complexity of patterns, as an additional parameter with the heat map. This additional parameter is used to evaluate the degree of influence of a scanpath within a dataset. More specifically, the 2D Cartesian coordinates of a scanpath are fitted to a space filling 1D fractal curve to characterise its temporal FD. The FDs of the scanpaths are then compared to match their geometric complexity to one another. The findings indicate that the FD can be a beneficial additional parameter when evaluating the candidacy of poorly matching scanpaths as outliers and performs better at identifying unusual scanpaths than using other methods, including scanpath matching, Jaccard, or bounding box methods alone.
A Long Hard Look at MWEs in the Age of Language Models

In recent years, language models (LMs) have become almost synonymous with NLP.Pre-trained to "read" a large text corpus, such models are useful as both a representation layer as well as a source of world knowledge.But how well do they represent MWEs?This talk will discuss various problems in representing MWEs, and the extent to which LMs address them:
Tag Assisted Neural Machine Translation of Film Subtitles

We implemented a neural machine translation system that uses automatic sequence tagging to improve the quality of translation.Instead of operating on unannotated sentence pairs, our system uses pre-trained tagging systems to add linguistic features to source and target sentences.Our proposed neural architecture learns a combined embedding of tokens and tags in the encoder, and simultaneous token and tag prediction in the decoder.Compared to a baseline with unannotated training, this architecture increased the BLEU score of German to English film subtitle translation outputs by 1.61 points using named entity tags; however, the BLEU score decreased by 0.38 points using part-of-speech tags.This demonstrates that certain token-level tag outputs from off-theshelf tagging systems can improve the output of neural translation systems using our combined embedding and simultaneous decoding extensions.
LOGICAL FALLACIES ON STUDENTS’ ARGUMENTATIVE WRITING

In writing an argumentative paragraph, there will be some fallacies that can be explained as students’ misunderstanding in interpreting source language to target language, in this context English is as a target language. This misunderstanding called logical fallacies where it can broke the meaning in TL. Therefore, the aim of this research was to identify and analyze types of logical fallacies done by the fourth semester students in argumentative writing at English department of IAIN Bukittinggi. The design of this research was descriptive quantitative. The population was the fourth semester students who take writing for professional context subject. The sample was 28 students. The instrument used was documentation of student’s argumentative writing tasks. The result showed that, some informal logical fallacies found in students’ writing. 76% students wrote inductive fallacy on their writing which means that students drew a conclusion without providing enough evidence on their writing. &#x0D;
Automatic Extraction of Semantic Roles in Support Verb Constructions

This paper deals with paraphrastic relations in Italian. In the following sentences: (a) Max strappò delle lacrime a Sara 'Max moved Sara to tears' and (b) Max fece piangere Sara 'Max made Sara cry', the verbs differ syntactically and semantically. Strappare 'tear/rip/wring' is transitive, fare ‘have/make’ is a causative, and piangere 'cry' is intransitive. Despite this, a translation of (a) as (b) is legitimate and therefore (a) is a paraphrase of (b). In theoretical linguistics this raises an issue concerning the relationship between strappare and fare/piangere in Italian, and that in English between move and make. In computational linguistics, can such paraphrases be obtained automatically? Which apparatus should be deployed? The aim of this paper is to suggest a pathway with which to answer these questions.
Uppsala NLP at SemEval-2021 Task 2: Multilingual Language Models for Fine-tuning and Feature Extraction in Word-in-Context Disambiguation

We describe the Uppsala NLP submission to SemEval-2021 Task 2 on multilingual and cross-lingual word-in-context disambiguation.We explore the usefulness of three pre-trained multilingual language models, XLM-RoBERTa (XLMR), Multilingual BERT (mBERT) and multilingual distilled BERT (mDistilBERT).We compare these three models in two setups, fine-tuning and as feature extractors.In the second case we also experiment with using dependency-based information.We find that fine-tuning is better than feature extraction.XLMR performs better than mBERT in the cross-lingual setting both with fine-tuning and feature extraction, whereas these two models give a similar performance in the multilingual setting.mDis-tilBERT performs poorly with fine-tuning but gives similar results to the other models when used as a feature extractor.We submitted our two best systems, fine-tuned with XLMR and mBERT.
Turn-Level User Satisfaction Estimation in E-commerce Customer Service

User satisfaction estimation in the dialoguebased customer service is critical not only for helping developers find the system defects, but also making it possible to get timely human intervention for dissatisfied customers.In this paper, we investigate the problem of user satisfaction estimation in E-commerce customer service.In order to apply the estimator to online services for timely human intervention, we need to estimate the satisfaction score at each turn.However, in actual scenario we can only collect the satisfaction labels for the whole dialogue sessions via user feedback.To this end, we formalize the turn-level satisfaction estimation as a reinforcement learning problem, in which the model can be optimized with only session-level satisfaction labels.We conduct experiments on the dataset collected from a commercial customer service system, and compare our model with the supervised learning models.Extensive experiments show that the proposed method outperforms all the baseline models.
AttesTable at SemEval-2021 Task 9: Extending Statement Verification with Tables for Unknown Class, and Semantic Evidence Finding

This paper describes our approach for Task 9 of SemEval 2021: Statement Verification and Evidence Finding with Tables.We participated in both subtasks, namely statement verification and evidence finding.For the subtask of statement verification, we extend the TAPAS model to adapt to the 'unknown' class of statements by finetuning it on an augmented version of the task data.For the subtask of evidence finding, we finetune the DistilBERT model in a Siamese setting.
Language Learning Strategies Used in Improving Writing Skills by Selected Form 4 ESL Learners

Language learning strategies (LLS) are very important in improving language skills especially writing.Having good writing skills is an attribute of a successful language learner.Certain learners will employ different learning strategies to acquire the skills.As writing is a crucial part in language learning, exploring the language learning strategies by ESL learners are worth reviewing especially in the context of young adult learners.Therefore, this study aimed to identify and explore the LLS commonly used to improve English writing by form 4 ESL learners in a secondary school in Penang.30 students were purposely selected as the respondents in the study.A questionnaire adapted from Strategy Inventory for Language Learning (SILL) by Oxford (1990) was distributed to the students to identify their use of strategies in improving writing skills.The questionnaire was divided into two parts, with the first part aimed to gain demographic information about the learners and the second part, that consist of 30 items, attempted to obtain data about the strategies used.The respondents were required to respond to the items with a Likert scale of 1 to 4 points.The data collected was analysed with SPSS 26 software application and discussion of the findings was done.The findings revealed that metacognitive strategies are the most frequently applied strategies while affective strategies are the least applied in improving English writing skills by the Form 4 ESL learners.For further research, it is recommended to conduct a study to determine the factors affecting the LLS used by the ESL learners.
Large-Scale English-Japanese Simultaneous Interpretation Corpus: Construction and Analyses with Sentence-Aligned Data

This paper describes the construction of a new large-scale English-Japanese Simultaneous Interpretation (SI) corpus and presents the results of its analysis.A portion of the corpus contains SI data from three interpreters with different amounts of experience.Some of the SI data were manually aligned with the source speeches at the sentence level.Their latency, quality, and word order aspects were compared among the SI data themselves as well as against offline translations.The results showed that (1) interpreters with more experience controlled the latency and quality better, and (2) large latency hurt the SI quality.
INTERINFLUENCE OF TEACHER’S PERSONAL QUALIITIES AND THE EFFICIENCY OF MASTERING LEARNING MATERIAL BY STUDENTS

This article focuses on the teachers’ required professional and personal qualities which are important for training highly qualified and competitive professionals. University professors greatly influence the formation of future specialist’s personality not only by knowledge, habits, attitudes towards others. It should be noted that teacher’s emotional orientation is an important factor of cooperation in educational process. Much attention is paid to positive worldview of the person, optimism which helps to increase desire to achieve success avoiding concentration on failures. The author explains the influence of positive and negative mood on learning the material by students. An interesting fact is that sometimes high spirits distract students from learning the material, especially when it is not interesting and not important. Negative emotions direct a student at searching the ways of problem solving, distract thoughts from the material in a class. The author indicates that the content of learning materials should bear some emotional potential. Information should physiologically activate and emotionally touch the student in order to be accepted and well memorised. Different examples of emotional strategies which could be used in practice teaching different disciplines are given in the article. The research represents an example of the lecture structure at Harvard Business School. The author mentions that when assessing pedagogical mastership, attention should be paid to: a) informational and logical characteristics of the lecture; b) activation of students’ mental activity in class; c) emotional character of the lecture; d) visualization. It specifies which tasks are better to be set, which questions to discuss, how to better finish the lecture, etc. It should be noted, that university teacher’s successful professional activity can be characterised by four main blocks of skills: 1) the methods of thinking (creativity, problem solving, critical thinking, lifelong learning); 2) the means of work (informational culture, understanding and active usage of digital technologies); 3) the methods of work (creativity, collaboration); 4) the skills necessary for successful life-activity (civic position, personal and social responsibility). The author represents the examples of the American universities questionaries “Students course assessment” and “Defining teaching ranking”. It is worth emphasizing that these results of course evaluation and pedagogical activity assessment are available to general public. It will be useful for students when selecting a course and a teacher as well as for teachers themselves. From these questionnaires teachers can learn students’ attitude to their course and to their personality as a teacher. R
EMPIRICAL ANALYSIS ON THE REDUCTION OF SPORTS INJURY BY FUNCTIONAL MOVEMENT SCREENING METHOD UNDER BIOLOGICAL IMAGE DATA

ABSTRACT Introduction: Sports recognition technology gradually mature. Among them, wearable sensors have attracted wide attention because of their accurate recognition. Objective: The following squats are used as an example to determine whether CNN and EMG signals determine whether functional motion is standard. Methods: Based on the FMS of EMG, 80 students of the same grade are randomly selected from the Physical Education School of XX University for the experiment and the results are verified. Results: The results show that the GBC can classify the EMG signal of the three functional movements more accurately, and the classification accuracy rate of squat, stride, and straight lunge squat is 91%, 89%, and 90%, respectively. The decision tree has a good ability to judge whether the functional movement is standard or not, and the accuracy of judgment can reach more than 98%. In conclusion, EMG-based FMS can effectively detect early sports injuries and plays a good role in reducing sports injuries. Conclusions: The classification effect of the squat is the most obvious, reaching 91%, and its recognition ability is the strongest. Level of evidence II; Therapeutic studies - investigation of treatment results.