ymoslem/acl-abstracts huggingface dataset:
SI2M \& AIOX Labs at WANLP 2022 Shared Task: Propaganda Detection in Arabic, A Data Augmentation and Name Entity Recognition Approach (2022)

This paper presents SI2M {\&} AIOX Labs work among the propaganda detection in Arabic text shared task. The objective of this challenge is to identify the propaganda techniques used in specific propaganda fragments. We use a combination of data augmentation, Name Entity Recognition, rule-based repetition detection, and ARBERT prediction to develop our system. The model we provide scored 0.585 micro F1-Score and ranked 6th out of 12 teams.
iCompass at WANLP 2022 Shared Task: ARBERT and MARBERT for Multilabel Propaganda Classification of Arabic Tweets (2022)

Arabic propaganda detection in Arabic was carried out using transformers pre-trained models ARBERT, MARBERT. They were fine-tuned for the down-stream task in hand {\textquoteleft}subtask 1', multilabel classification of Arabic tweets. Submitted model was MARBERT the got 0.597 micro F1 score and got the fifth rank.
ChavanKane at WANLP 2022 Shared Task: Large Language Models for Multi-label Propaganda Detection (2022)

The spread of propaganda through the internet has increased drastically over the past years. Lately, propaganda detection has started gaining importance because of the negative impact it has on society. In this work, we describe our approach for the WANLP 2022 shared task which handles the task of propaganda detection in a multi-label setting. The task demands the model to label the given text as having one or more types of propaganda techniques. There are a total of 21 propaganda techniques to be detected. We show that an ensemble of five models performs the best on the task, scoring a micro-F1 score of 59.73{\%}. We also conduct comprehensive ablations and propose various future directions for this work.
AraBERT Model for Propaganda Detection (2022)

Nowadays, the rapid dissemination of data on digital platforms has resulted in the emergence of information pollution and data contamination, specifically mis-information, mal-information, dis-information, fake news, and various types of propaganda. These topics are now posing a serious threat to the online digital realm, posing numerous challenges to social media platforms and governments around the world. In this article, we propose a propaganda detection model based on the transformer-based model AraBERT, with the objective of using this framework to detect propagandistic content in the Arabic social media text scene, well with purpose of making online Arabic news and media consumption healthier and safer. Given the dataset, our results are relatively encouraging, indicating a huge potential for this line of approaches in Arabic online news text NLP.
AraBEM at WANLP 2022 Shared Task: Propaganda Detection in Arabic Tweets (2022)

Propaganda is information or ideas that an organized group or government spreads to influence people{\'s} opinions, especially by not giving all the facts or secretly emphasizing only one way of looking at the points. The ability to automatically detect propaganda-related linguistic signs is a challenging task that researchers in the NLP community have recently started to address. This paper presents the participation of our team AraBEM in the propaganda detection shared task on Arabic tweets. Our system utilized a pre-trained BERT model to perform multi-class binary classification. It attained the best score at 0.602 micro-f1, ranking third on subtask-1, which identifies the propaganda techniques as a multilabel classification problem with a baseline of 0.079.
IITD at WANLP 2022 Shared Task: Multilingual Multi-Granularity Network for Propaganda Detection (2022)

We present our system for the two subtasks of the shared task on propaganda detection in Arabic, part of WANLP`2022. Subtask 1 is a multi-label classification problem to find the propaganda techniques used in a given tweet. Our system for this task uses XLM-R to predict probabilities for the target tweet to use each of the techniques. In addition to finding the techniques, subtask 2 further asks to identify the textual span for each instance of each technique that is present in the tweet; the task can be modelled as a sequence tagging problem. We use a multi-granularity network with mBERT encoder for subtask 2. Overall, our system ranks second for both subtasks (out of 14 and 3 participants, respectively). Our experimental results and analysis show that it does not help to use a much larger English corpus annotated with propaganda techniques, regardless of whether used in English or after translation to Arabic.
Pythoneers at WANLP 2022 Shared Task: Monolingual AraBERT for Arabic Propaganda Detection and Span Extraction (2022)

In this paper, we present two deep learning approaches that are based on AraBERT, submitted to the Propaganda Detection shared task of the Seventh Workshop for Arabic Natural Language Processing (WANLP 2022). Propaganda detection consists of two main sub-tasks, mainly propaganda identification and span extraction. We present one system per sub-task. The first system is a Multi-Task Learning model that consists of a shared AraBERT encoder with task-specific binary classification layers. This model is trained to jointly learn one binary classification task per propaganda method. The second system is an AraBERT model with a Conditional Random Field (CRF) layer. We achieved rank 3 on the first sub-task and rank 1 on the second sub-task.
CNLP-NITS-PP at WANLP 2022 Shared Task: Propaganda Detection in Arabic using Data Augmentation and AraBERT Pre-trained Model (2022)

In today`s time, online users are regularly exposed to media posts that are propagandistic. Several strategies have been developed to promote safer media consumption in Arabic to combat this. However, there is a limited available multilabel annotated social media dataset. In this work, we have used a pre-trained AraBERT twitter-base model on an expanded train data via data augmentation. Our team CNLP-NITS-PP, has achieved the third rank in subtask 1 at WANLP-2022, for propaganda detection in Arabic (shared task) in terms of micro-F1 score of 0.602.
NGU CNLP atWANLP 2022 Shared Task: Propaganda Detection in Arabic (2022)

This paper presents the system developed by the NGU{\_}CNLP team for addressing the shared task on Propaganda Detection in Arabic at WANLP 2022. The team participated in the shared tasks' two sub-tasks which are: 1) Propaganda technique identification in text and 2) Propaganda technique span identification. In the first sub-task, the goal is to detect all employed propaganda techniques in some given piece of text out of a possible 17 different techniques or to detect that no propaganda technique is being used in that piece of text. As such, this first sub-task is a multi-label classification problem with a pool of 18 possible labels. Subtask 2 extends sub-task 1, by requiring the identification of the exact text span in which a propaganda technique was employed, making it a sequence labeling problem. For task 1, a combination of a data augmentation strategy coupled with an enabled transformer-based model comprised our classification model. This classification model ranked first amongst the 14 systems participating in this subtask. For sub-task two, a transfer learning model was adopted. The system ranked third among the 3 different models that participated in this subtask.
Findings of the VarDial Evaluation Campaign 2022 (2022)

This report presents the results of the shared tasks organized as part of the VarDial Evaluation Campaign 2022. The campaign is part of the ninth workshop on Natural Language Processing (NLP) for Similar Languages, Varieties and Dialects (VarDial), co-located with COLING 2022. Three separate shared tasks were included this year: Identification of Languages and Dialects of Italy (ITDI), French Cross-Domain Dialect Identification (FDI), and Dialectal Extractive Question Answering (DialQA). All three tasks were organized for the first time this year.
Social Context and User Profiles of Linguistic Variation on a Micro Scale (2022)

This paper presents a new tweet-based approach in geolinguistic analysis which combines geolocation, user IDs and textual features in order to identify patterns of linguistic variation on a sub-city scale. Sub-city variations can be connected to social drivers and thus open new opportunities for understanding the mechanisms of language variation and change. However, measuring linguistic variation on these scales is challenging due to the lack of highly-spatially-resolved data as well as to the daily movement or users' {\textquotedblleft}mobility{\textquotedblright} inside cities which can obscure the relation between the social context and linguistic variation. Here we demonstrate how combining geolocation with user IDs and textual analysis of tweets can yield information about the linguistic profiles of the users, the social context associated with specific locations and their connection to linguistic variation. We apply our methodology to analyze dialects in Buenos Aires and find evidence of socially-driven variation. Our methods will contribute to the identification of sociolinguistic patterns inside cities, which are valuable in social sciences and social services.
dialectR: Doing Dialectometry in R (2022)

We present dialectR, an open-source R package for performing quantitative analyses of dialects based on categorical measures of difference and on variants of edit distance. dialectR stands as one of the first programmable toolkits that may freely be combined and extended by users with further statistical procedures. We describe implementational details of the package, and provide two examples of its use: one performing analyses based on multidimensional scaling and hierarchical clustering on a dataset of Dutch dialects, and another showing how an approximation of the acoustic vowel space may be achieved by performing an MFCC (Mel-Frequency Cepstral Coefficients)-based acoustic distance on audio recordings of vowels.
Low-Resource Neural Machine Translation: A Case Study of Cantonese (2022)

The development of Natural Language Processing (NLP) applications for Cantonese, a language with over 85 million speakers, is lagging compared to other languages with a similar number of speakers. In this paper, we present, to our best knowledge, the first benchmark of multiple neural machine translation (NMT) systems from Mandarin Chinese to Cantonese. Additionally, we performed parallel sentence mining (PSM) as data augmentation for the extremely low resource language pair and increased the number of sentence pairs from 1,002 to 35,877. Results show that with PSM, the best performing model (BPE-level bidirectional LSTM) scored 11.98 BLEU better than the vanilla baseline and 9.93 BLEU higher than our strong baseline. Our unsupervised NMT (UNMT) results also refuted previous assumption n (Rubino et al., 2020) that the poor performance was related to the lack of linguistic similarities between the target and source languages, particularly in the case of Cantonese and Mandarin. In the process of building the NMT system, we also created the first large-scale parallel training and evaluation datasets of the language pair. Codes and datasets are publicly available at \url{https://github.com/evelynkyl/yue_nmt}.
Phonetic, Semantic, and Articulatory Features in Assamese-Bengali Cognate Detection (2022)

In this paper, we propose a method to detect if words in two similar languages, Assamese and Bengali, are cognates. We mix phonetic, semantic, and articulatory features and use the cognate detection task to analyze the relative informational contribution of each type of feature to distinguish words in the two similar languages. In addition, since support for low-resourced languages like Assamese can be weak or nonexistent in some multilingual language models, we create a monolingual Assamese Transformer model and explore augmenting multilingual models with monolingual models using affine transformation techniques between vector spaces.
Mapping Phonology to Semantics: A Computational Model of Cross-Lingual Spoken-Word Recognition (2022)

Closely related languages are often mutually intelligible to various degrees. Therefore, speakers of closely related languages are usually capable of (partially) comprehending each other`s speech without explicitly learning the target, second language. The cross-linguistic intelligibility among closely related languages is mainly driven by linguistic factors such as lexical similarities. This paper presents a computational model of spoken-word recognition and investigates its ability to recognize word forms from different languages than its native, training language. Our model is based on a recurrent neural network that learns to map a word`s phonological sequence onto a semantic representation of the word. Furthermore, we present a case study on the related Slavic languages and demonstrate that the cross-lingual performance of our model not only predicts mutual intelligibility to a large extent but also reflects the genetic classification of the languages in our study.
Annotating Norwegian language varieties on Twitter for Part-of-speech (2022)

Norwegian Twitter data poses an interesting challenge for Natural Language Processing (NLP) tasks. These texts are difficult for models trained on standardized text in one of the two Norwegian written forms (Bokm{\r{a}}l and Nynorsk), as they contain both the typical variation of social media text, as well as a large amount of dialectal variety. In this paper we present a novel Norwegian Twitter dataset annotated with POS-tags. We show that models trained on Universal Dependency (UD) data perform worse when evaluated against this dataset, and that models trained on Bokm{\r{a}}l generally perform better than those trained on Nynorsk. We also see that performance on dialectal tweets is comparable to the written standards for some models. Finally we perform a detailed analysis of the errors that models commonly make on this data.
OcWikiDisc: a Corpus of Wikipedia Talk Pages in Occitan (2022)

This paper presents OcWikiDisc, a new freely available corpus in Occitan, as well as language identification experiments on Occitan done as part of the corpus building process. Occitan is a regional language spoken mainly in the south of France and in parts of Spain and Italy. It exhibits rich diatopic variation, it is not standardized, and it is still low-resourced, especially when it comes to large downloadable corpora. We introduce OcWikiDisc, a corpus extracted from the talk pages associated with the Occitan Wikipedia. The version of the corpus with the most restrictive language filtering contains 8K user messages for a total of 618K tokens. The language filtering is performed based on language identification experiments with five off-the-shelf tools, including the new fasttext`s language identification model from Meta AI`s No Language Left Behind initiative, released in July 2022.
Is Encoder-Decoder Transformer the Shiny Hammer? (2022)

We present an approach to multi-class classification using an encoder-decoder transformer model. We trained a network to identify French varieties using the same scripts we use to train an encoder-decoder machine translation model. With some slight modification to the data preparation and inference parameters, we showed that the same tools used for machine translation can be easily re-used to achieve competitive performance for classification. On the French Dialectal Identification (FDI) task, we scored 32.4 on weighted F1, but this is far from a simple naive bayes classifier that outperforms a neural encoder-decoder model at 41.27 weighted F1.
The Curious Case of Logistic Regression for Italian Languages and Dialects Identification (2022)

Automatic Language Identification represents an important task for improving many real-world applications such as opinion mining and machine translation. In the case of closely-related languages such as regional dialects, this task is often challenging. In this paper, we propose an extensive evaluation of different approaches for the identification of Italian dialects and languages, spanning from classical machine learning models to more complex neural architectures and state-of-the-art pre-trained language models. Surprisingly, shallow machine learning models managed to outperform huge pre-trained language models in this specific task. This work was developed in the context of the Identification of Languages and Dialects of Italy (ITDI) task organised at VarDial 2022 Evaluation Campaign. Our best submission managed to achieve a weighted F1-score of 0.6880, ranking 5th out of 9 final submissions.
Neural Networks for Cross-domain Language Identification. Phlyers @Vardial 2022 (2022)

We present our contribution to the Identification of Languages and Dialects of Italy shared task (ITDI) proposed in the VarDial Evaluation Campaign 2022, which asked participants to automatically identify the language of a text associated to one of the language varieties of Italy. The method that yielded the best results in our experiments was a Deep Feedforward Neural Network (DNN) trained on character ngram counts, which provided a better performance compared to Naive Bayes methods and Convolutional Neural Networks (CNN). The system was among the best methods proposed for the ITDI shared task. The analysis of the results suggests that simple DNNs could be more efficient than CNNs to perform language identification of close varieties.
Transfer Learning Improves French Cross-Domain Dialect Identification: NRC @ VarDial 2022 (2022)

We describe the systems developed by the National Research Council Canada for the French Cross-Domain Dialect Identification shared task at the 2022 VarDial evaluation campaign. We evaluated two different approaches to this task: SVM and probabilistic classifiers exploiting n-grams as features, and trained from scratch on the data provided; and a pre-trained French language model, CamemBERT, that we fine-tuned on the dialect identification task. The latter method turned out to improve the macro-F1 score on the test set from 0.344 to 0.430 (25{\%} increase), which indicates that transfer learning can be helpful for dialect identification.
Italian Language and Dialect Identification and Regional French Variety Detection using Adaptive Naive Bayes (2022)

This article describes the language identification approach used by the SUKI team in the Identification of Languages and Dialects of Italy and the French Cross-Domain Dialect Identification shared tasks organized as part of the VarDial workshop 2022. We describe some experiments and the preprocessing techniques we used for the training data in preparation for the shared task submissions, which are also discussed. Our Naive Bayes-based adaptive system reached the first position in Italian language identification and came second in the French variety identification task.
Pre-trained Language Models' Interpretation of Evaluativity Implicature: Evidence from Gradable Adjectives Usage in Context (2022)

By saying Maria is tall, a human speaker typically implies that Maria is evaluatively tall from the speaker`s perspective. However, by using a different construction Maria is taller than Sophie, we cannot infer from Maria and Sophie`s relative heights that Maria is evaluatively tall because it is possible for Maria to be taller than Sophie in a context in which they both count as short. Can pre-trained language models (LMs) {\textquotedblleft}understand{\textquotedblright} evaulativity (EVAL) inference? To what extent can they discern the EVAL salience of different constructions in a conversation? Will it help LMs' implicitness performance if we give LMs a persona such as chill, social, and pragmatically skilled? Our study provides an approach to probing LMs' interpretation of EVAL inference by incorporating insights from experimental pragmatics and sociolinguistics. We find that with the appropriate prompt, LMs can succeed in some pragmatic level language understanding tasks. Our study suggests that socio-pragmatics methodology can shed light on the challenging questions in NLP.
Pragmatic and Logical Inferences in NLI Systems: The Case of Conjunction Buttressing (2022)

An intelligent system is expected to perform reasonable inferences, accounting for both the literal meaning of a word and the meanings a word can acquire in different contexts. A specific kind of inference concerns the connective and, which in some cases gives rise to a temporal succession or causal interpretation in contrast with the logic, commutative one (Levinson, 2000). In this work, we investigate the phenomenon by creating a new dataset for evaluating the interpretation of and by NLI systems, which we use to test three Transformer-based models. Our results show that all systems generalize patterns that are consistent with both the logical and the pragmatic interpretation, perform inferences that are inconsistent with each other, and show clear divergences with both theoretical accounts and humans' behavior.
\textquotedblleftDevils Are in the Details\textquotedblright: Annotating Specificity of Clinical Advice from Medical Literature (2022)

Prior studies have raised concerns over specificity issues in clinical advice. Lacking specificity {---} explicitly discussed detailed information {---} may affect the quality and implementation of clinical advice in medical practice. In this study, we developed and validated a fine-grained annotation schema to describe different aspects of specificity in clinical advice extracted from medical research literature. We also presented our initial annotation effort and discussed future directions towards an NLP-based specificity analysis tool for summarizing and verifying the details in clinical advice.
Searching for PETs: Using Distributional and Sentiment-Based Methods to Find Potentially Euphemistic Terms (2022)

This paper presents a linguistically driven proof of concept for finding potentially euphemistic terms, or PETs. Acknowledging that PETs tend to be commonly used expressions for a certain range of sensitive topics, we make use of distri- butional similarities to select and filter phrase candidates from a sentence and rank them using a set of simple sentiment-based metrics. We present the results of our approach tested on a corpus of sentences containing euphemisms, demonstrating its efficacy for detecting single and multi-word PETs from a broad range of topics. We also discuss future potential for sentiment-based methods on this task.
Named Entity Recognition as Structured Span Prediction (2022)

Named Entity Recognition (NER) is an important task in Natural Language Processing with applications in many domains. While the dominant paradigm of NER is sequence labelling, span-based approaches have become very popular in recent times but are less well understood. In this work, we study different aspects of span-based NER, namely the span representation, learning strategy, and decoding algorithms to avoid span overlap. We also propose an exact algorithm that efficiently finds the set of non-overlapping spans that maximizes a global score, given a list of candidate spans. We performed our study on three benchmark NER datasets from different domains. We make our code publicly available at \url{https://github.com/urchade/span-structured-prediction}.
Global Span Selection for Named Entity Recognition (2022)

Named Entity Recognition (NER) is an important task in Natural Language Processing with applications in many domains. In this paper, we describe a novel approach to named entity recognition, in which we output a set of spans (i.e., segmentations) by maximizing a global score. During training, we optimize our model by maximizing the probability of the gold segmentation. During inference, we use dynamic programming to select the best segmentation under a linear time complexity. We prove that our approach outperforms CRF and semi-CRF models for Named Entity Recognition. We will make our code publicly available.