Reviewerly/exHarmony huggingface dataset:
SWOT -AHP AS A METHOD OF PUBLIC SERVICE STRATEGY

SWOT-AHP helps companies to develop and plan strategic alternatives to overcome all the challenges faced. The purpose of this study is to determine the public service strategy of the Makassar Laboratory Center. This research is a quantitative research, using the SWOT-AHP approach to determine the right strategy in improving organizational performance. Data is collected from the documentation results and then analyzed according to SWOT-AHP stages. The results of data processing show that in the item of strength, having an international standard TB lab is the most dominant strength possessed by BBLK Makassar, in the item of weakness, the biggest weakness is the organizational culture that still tends to be negative (rigid, individual, passive, lack of empathy in providing services, so efforts need to be made for the organization to carry out public services based on public excellent. While the most dominant opportunity to be utilized is information technology support, while the biggest threat faced by BBLK Makassar is the entry of foreign investors in laboratory services that have more sophisticated systems and equipment (the threat of global competition in the AFTA era). After the SWOT-AHP analysis, 4 strategies emerged, namely: increasing the promotion and technology-based information services, building an integrated information technology system, improving the quality and quantity of human resources, and maximizing tool utilization by adding new types of examinations.
Strategi Baca Tanya Kerja (BTK) dengan Model Portofolio untuk Meningkatkan Belajar Mata Pelajaran Sejarah bagi Anak Berkebutuhan Khusus (ABK) di SMA Negeri 4 Sidoarjo

The purpose of enacting inclusive education in normal schools is so that there is no discrimination in the world of education so that children with special needs can study in regular schools. With this policy, it is certainly not easy for History teachers to be able to apply methods and strategies for children with special needs and regular children whose class has become one. Therefore it is necessary to have a work question reading strategy (BTK) with a portfolio model for children with special needs when learning takes place. Children with special needs do not always experience problems in learning, but when interacting with peers in regular classes there are certain things that must get special attention from the teacher, especially the supervising teacher to get optimal learning results. The method used is classroom action research. The results of the study show that the BTK strategy can improve learning outcomes and also that portfolio-based learning proves that it can improve the learning outcomes of children with special needs in history subjects.
VISUALISASI DATA SEBARAN WILAYAH PARIWISATA DI PROVINSI BALI DENGAN PLATFORM TABLEAU

Tourism is one of the influential factors in the economy of Indonesia. In general, tourism refers to the journey undertaken by an individual within a certain period from one place to another. The tourism potential in Indonesia has been rapidly growing, especially since the Covid-19 pandemic. Based on this issue, the author attempts to find data regarding the distribution of tourist locations in the Province of Bali to identify the potential for sustainable tourism in each area. Once the data is collected, the author visualizes it to make it easily understandable and to serve as a reference for the equitable distribution of tourism potential in Bali. Tableau, a data visualization tool, is used to create various types of diagrams and maps. The research results indicate that the tourist attractions Penglipuran, Goa Lawah, and Jatiluwih are the top three with the highest ratings. Bali zoo, GWK, and Kuta Beach are the three tourist attractions with the most reviews on Google. This Tableau visualization can assist tourists in determining their desired travel destinations.
Fake Face Detection in Identity Cards Using Stegoface

Abstract: Identification and Machine-Readable Travel Documents (MRTDs) play a crucial role in verifying and validating identities in various situations, such as international travel, civil applications, online commerce, and access to transaction processing systems. These documents incorporate multiple security features aimed at preventing document forgery. However, criminals have shifted their focus to obtaining genuine documents fraudulently and manipulating facial portraits, as the existing security systems are challenging to bypass. To address this issue and mitigate the risks associated with such fraud, it is imperative for governments and ID/MRTD manufacturers to continually enhance and develop security measures. In this context, we present StegoFace, an innovative and efficient steganography method specifically designed for concealing secret messages within facial images found on common IDs and MRTDs. StegoFace employs an end-to-end approach, consisting of an ensemble of n Deep Convolutional Auto Encoders, which encode the secret message into a stegofacial image, and a Deep Convolutional Auto Decoder, capable of extracting the hidden message from the stegofacial image. Notably, our StegoFace approach outperforms the StegaStamp method in terms of perceptual quality, as evidenced by the results of metrics such as Peak Signal-to-Noise Ratio, hiding capacity, and imperceptibility on the test dataset.
FEED PETs: Further Experimentation and Expansion on the Disambiguation of Potentially Euphemistic Terms

Transformers have been shown to work well for the task of English euphemism disambiguation, in which a potentially euphemistic term (PET) is classified as euphemistic or non-euphemistic in a particular context.In this study, we expand on the task in two ways.First, we annotate PETs for vagueness, a linguistic property associated with euphemisms, and find that transformers are generally better at classifying vague PETs, suggesting linguistic differences in the data that impact performance.Second, we present novel euphemism corpora in three different languages: Yoruba, Spanish, and Mandarin Chinese.We perform euphemism disambiguation experiments in each language using multilingual transformer models mBERT and XLM-RoBERTa, establishing preliminary results from which to launch future work.
BpHigh at WASSA 2023: Using Contrastive Learning to build Sentence Transformer models for Multi-Class Emotion Classification in Code-mixed Urdu

In this era of digital communication and social media, texting and chatting among individuals occur mainly through code-mixed or Romanized versions of the native language prevalent in the region.The presence of Romanized and code-mixed language develops the need to build NLP systems in these domains to leverage the digital content for various use cases.This paper describes our contribution to the subtask MCEC of the shared task WASSA 2023:Shared Task on Multi-Label and Multi-Class Emotion Classification on Code-Mixed Text Messages.We explore how one can build sentence transformers models for low-resource languages using unsupervised data by leveraging contrastive learning techniques described in the SIMCSE paper and using the sentence transformer developed to build classification models using the SetFit approach.Additionally, we'll publish our code and models on GitHub and Hugging-Face, two open-source hosting services.
Tü-CL at SIGMORPHON 2023: Straight-Through Gradient Estimation for Hard Attention

This paper describes our systems participating in the 2023 SIGMORPHON Shared Task on Morphological Inflection (Goldman et al., 2023) and in the 2023 SIGMORPHON Shared Task on Interlinear Glossing.We propose methods to enrich predictions from neural models with discrete, i.e. interpretable, information.For morphological inflection, our models learn deterministic mappings from subsets of source lemma characters and morphological tags to individual target characters, which introduces interpretability.For interlinear glossing, our models learn a shallow morpheme segmentation in an unsupervised way jointly with predicting glossing lines.Estimated segmentation may be useful when no ground-truth segmentation is available.As both methods introduce discreteness into neural models, our technical contribution is to show that straight-through gradient estimators are effective to train hard attention models.
CRAPES:Cross-modal Annotation Projection for Visual Semantic Role Labeling

Automatic image comprehension is an important yet challenging task that includes identifying actions in an image and corresponding action participants.Most current approaches to this task, now termed Grounded Situation Recognition (GSR), start by predicting a verb that describes the action and then predict the nouns that can participate in the action as arguments to the verb.This problem formulation limits each image to a single action even though several actions could be depicted.In contrast, text-based Semantic Role Labeling (SRL) aims to label all actions in a sentence, typically resulting in at least two or three predicate argument structures per sentence.We hypothesize that expanding GSR to follow the more liberal SRL text-based approach to action and participant identification could improve image comprehension results.To test this hypothesis and to preserve generalization capabilities, we use general-purpose vision and language components as a front-end.This paper presents our results, a substantial 28.6 point jump in performance on the SWiG dataset, which confirm our hypothesis.We also discuss the benefits of loosely coupled broad-coverage off-the-shelf components which generalized well to out of domain images, and can decrease the need for manual image semantic role annotation.
Fine-tuning mSLAM for the SIGMORPHON 2022 Shared Task on Grapheme-to-Phoneme Conversion

Grapheme-to-phoneme (G2P) conversion is a task that is inherently related to both written and spoken language.Therefore, our submission to the G2P shared task builds off of mSLAM (Bapna et al., 2022), a 600M parameter encoder model pretrained simultaneously on text from 101 languages and speech from 51 languages.For fine-tuning a G2P model, we combined mSLAM's text encoder, which uses characters as its input tokens, with an uninitialized single-layer RNN-T decoder (Graves, 2012) whose vocabulary is the set of all 381 phonemes appearing in the shared task data.We took an explicitly multilingual approach to modeling the G2P tasks, fine-tuning and evaluating a single model that covered all the languages in each task, and adding language codes as prefixes to the input strings as a means of specifying the language of each example.
Guiding Zero-Shot Paraphrase Generation with Fine-Grained Control Tokens

Sequence-to-sequence paraphrase generation models often struggle with the generation of diverse paraphrases.This deficiency constrains the viability of leveraging paraphrase generation in different Natural Language Processing tasks.We propose a translation-based guided paraphrase generation model that learns useful features for promoting surface form variation in generated paraphrases from cross-lingual parallel data.Our proposed method leverages multilingual neural machine translation pretraining to learn zero-shot paraphrasing.Furthermore, we incorporate dedicated prefix tokens into the training of the machine translation models to promote variation.The prefix tokens are designed to affect various linguistic features related to surface form realizations, and can be applied during inference to guide the decoding process towards a desired solution.We assess the proposed guided model on paraphrase generation in three languages, English, Finnish, and Swedish, and provide analysis on the feasibility of the prefix tokens to guided paraphrasing.Our analysis suggests that the attributes represented by the prefix tokens are useful in promoting variation, by pushing the paraphrases generated by the guided model to diverge from the input sentence while preserving semantics conveyed by the sentence well.
Leverage Points in Modality Shifts: Comparing Language-only and Multimodal Word Representations

Multimodal embeddings aim to enrich the semantic information in neural representations of language compared to text-only models.While different embeddings exhibit different applicability and performance on downstream tasks, little is known about the systematic representation differences attributed to the visual modality.Our paper compares word embeddings from three vision-and-language models (CLIP, OpenCLIP and Multilingual CLIP, Radford et al. 2021;Ilharco et al. 2021;Carlsson et al. 2022) and three text-only models, with static (FastText, Bojanowski et al., 2017) as well as contextual representations (multilingual BERT Devlin et al. 2018; XLM-RoBERTa, Conneau et al. 2019).This is the first large-scale study of the effect of visual grounding on language representations, including 46 semantic parameters.We identify meaning properties and relations that characterize words whose embeddings are most affected by the inclusion of visual modality in the training data; that is, points where visual grounding turns out most important.We find that the effect of visual modality correlates most with denotational semantic properties related to concreteness, but is also detected for several specific semantic classes, as well as for valence, a sentiment-related connotational property of linguistic expressions.
Revisiting Syntax-Based Approach in Negation Scope Resolution

Negation scope resolution is the process of detecting the negated part of a sentence. Unlike the syntax-based approach employed in previous research, state-of-the-art methods performed better without the explicit use of syntactic structure. This work revisits the syntax-based approach and re-evaluates the effectiveness of syntactic structure in negation scope resolution. We replace the parser utilized in the prior works with state-of-the-art parsers and modify the syntax-based heuristic rules. The experimental results demonstrate that the simple modifications enhance the performance of the prior syntax-based method to the same level as state-of-the-art end-to-end neural-based methods.
Annotating and Training for Population Subjective Views

In this paper, we present a dataset of subjective views (beliefs and attitudes) held by individuals or groups. 1 We analyze the usefulness of the dataset by training a neural classifier that identifies belief-containing sentences that are relevant for our broader project of interest-scientific modeling of complex systems.We also explore and discuss difficulties related to annotation of subjective views and propose ways of addressing them.
Estimating Semantic Similarity between In-Domain and Out-of-Domain Samples

Prior work typically describes out-of-domain (OOD) or out-of-distribution (OODist) samples as those that originate from dataset(s) or source(s) different from the training set but for the same task.When compared to in-domain (ID) samples, the models have been known to usually perform poorer on OOD samples, although this observation is not consistent.Another thread of research has focused on OOD detection, albeit mostly using supervised approaches.In this work, we first consolidate and present a systematic analysis of multiple definitions of OOD and OODist as discussed in prior literature.Then, we analyze the performance of a model under ID and OOD/OODist settings in a principled way.Finally, we seek to identify an unsupervised method for reliably identifying OOD/OODist samples without using a trained model.The results of our extensive evaluation using 12 datasets from 4 different tasks suggest the promising potential of unsupervised metrics in this task.
Analyzing Syntactic Generalization Capacity of Pre-trained Language Models on Japanese Honorific Conversion

Using Japanese honorifics is challenging because it requires not only knowledge of the grammatical rules but also contextual information, such as social relationships.It remains unclear whether pre-trained large language models (LLMs) can flexibly handle Japanese honorifics like humans.To analyze this, we introduce an honorific conversion task that considers social relationships among people mentioned in a conversation.We construct a Japanese honorifics dataset from problem templates of various sentence structures to investigate the syntactic generalization capacity of GPT-3, one of the leading LLMs, on this task under two settings: fine-tuning and prompt learning.Our results showed that the fine-tuned GPT-3 performed better in a context-aware honorific conversion task than the prompt-based one.The fine-tuned model demonstrated overall syntactic generalizability towards compound honorific sentences, except when tested with the data involving direct speech.
Probing Out-of-Distribution Robustness of Language Models with Parameter-Efficient Transfer Learning

As the size of the pre-trained language model (PLM) continues to increase, numerous parameter-efficient transfer learning methods have been proposed recently to compensate for the tremendous cost of fine-tuning.Despite the impressive results achieved by large pretrained language models (PLMs) and various parameter-efficient transfer learning (PETL) methods on sundry benchmarks, it remains unclear if they can handle inputs that have been distributionally shifted effectively.In this study, we systematically explore how the ability to detect out-of-distribution (OOD) changes as the size of the PLM grows or the transfer methods are altered.Specifically, we evaluated various PETL techniques, including fine-tuning, Adapter, LoRA, and prefix-tuning, on three different intention classification tasks, each utilizing various language models with different scales.
An Ensembled Encoder-Decoder System for Interlinear Glossed Text

This paper presents a submission to Track 1 of the 2023 SIGMORPHON shared task on interlinear glossed text (IGT) (Ginn et al., 2023).There are a wide amount of techniques for building and training IGT models (see Moeller and Hulden, 2018;McMillan-Major, 2020;Zhao et al., 2020).We describe the system's ensembled sequence-to-sequence approach, perform experiments, and share the submission's test-set accuracy.We also discuss future areas of research in low-resource token classification methods for IGT.
Investigating Phoneme Similarity with Artificially Accented Speech

While the deep learning revolution has led to significant performance improvements in speech recognition, accented speech remains a challenge.Current approaches to this challenge typically do not seek to understand and provide explanations for the variations of accented speech, whether they stem from native regional variation or non-native error patterns.This paper seeks to address non-native speaker variations from both a knowledge-based and a data-driven perspective.We propose to approximate non-native accented-speech pronunciation patterns by the means of two approaches: based on phonetic and phonological knowledge on the one hand and inferred from a text-tospeech system on the other.Artificial speech is then generated with a range of variants which have been captured in confusion matrices representing phoneme similarities.We then show that non-native accent confusions actually propagate to the transcription from the ASR, thus suggesting that the inference of accent specific phoneme confusions is achievable from artificial speech.
LEXPLAIN: Improving Model Explanations via Lexicon Supervision

Model explanations that shed light on the model's predictions are becoming a desired additional output of NLP models, alongside their predictions.Challenges in creating these explanations include making them trustworthy and faithful to the model's predictions.In this work, we propose a novel framework for guiding model explanations by supervising them explicitly.To this end, our method, LEXPLAIN, uses task-related lexicons to directly supervise model explanations.This approach consistently improves the plausibility of model's explanations without sacrificing performance on the task, as we demonstrate on sentiment analysis and toxicity detection.Our analyses show that our method also demotes spurious correlations (i.e., with respect to African American English dialect) on toxicity detection, improving fairness.
PCFG-Based Natural Language Interface Improves Generalization for Controlled Text Generation

Existing work on controlled text generation (CTG) assumes a control interface of categorical attributes.In this work, we propose a natural language (NL) interface, where we craft a PCFG to embed the control attributes into natural language commands, and propose variants of existing CTG models that take commands as input.In our experiments, we design tailored setups to test the model's generalization abilities.We find our PCFG-based command generation approach is effective for handling unseen commands compared to fix-set templates.Further, our proposed NL models can effectively generalize to unseen attributes (a new ability enabled by the NL interface), as well as unseen attribute combinations.Interestingly, in model comparisons, the simple conditional generation approach, enhanced with our proposed NL interface, is shown to be a strong baseline in those challenging settings.
Generalized Glossing Guidelines: An Explicit, Human- and Machine-Readable, Item-and-Process Convention for Morphological Annotation

We introduce a YAML notation for multi-line interlinear glossed text (IGT) that represents non-concatentative processes such as infixation, reduplication, mutation, truncation, and tonal overwriting in a consistent, formally rigorous way, on par with affixation, using an Item-and-Process (IP) framework.Our new notation-Generalized Glossing Guidelines (GGG)-is human-and machine-readable and easy to edit with general purpose tools.A GGG representation has four fields: (1) A Surface Representation (sr) with curly brackets to show where non-concatenative morphological processes have applied.(2) A Lexical Representation (lx) that explicitly shows nonconcatenative processes as insertions, deletions, and substitutions as they apply to the basic form of morphemes.(3) A gloss field (gl) that associates glosses with morphemes and morphological processes in the sr and lx lines.(4) A metalanguage translation.We demonstrate the linguistic adequacy of GGG and compare it to two other IGT annotation schemes.
Linear Discriminative Learning: a competitive non-neural baseline for morphological inflection

This paper presents our submission to the SIG-MORPHON 2023 task 2 of Cognitively Plausible Morphophonological Generalization in Korean.We implemented both Linear Discriminative Learning and Transformer models and found that the Linear Discriminative Learning model trained on a combination of corpus and experimental data showed the best performance with the overall accuracy of around 83%.We found that the best model must be trained on both corpus data and the experimental data of one particular participant.Our examination of speaker-variability and speaker-specific information did not explain why a particular participant combined well with the corpus data.We recommend Linear Discriminative Learning models as a future non-neural baseline system, owning to its training speed, accuracy, model interpretability and cognitive plausibility.In order to improve the model performance, we suggest using bigger data and/or performing data augmentation and incorporating speakerand item-specifics considerably.
Not Just Iconic: Emoji Interpretation is Shaped by Use

Where do the meaning of emoji come from?Though it is often assumed that emoji are fully iconic, with meanings derived from their visual forms, we argue that this is only one component of their meaning.We surveyed users and non-users of the Chinese social media platform WeChat for their interpretations of emoji specific to WeChat.We find that some emoji show significant differences in their interpretations between users and non-users, as well as how familiar a person is with the specific emoji's use.We argue that this reflects a more complex process for building the meaning of emoji on a platform than pure iconicity.