ymoslem/acl-abstracts huggingface dataset:
Simple But Not Na\"ive: Fine-Grained Arabic Dialect Identification Using Only N-Grams (2019)

This paper presents the participation of Qatar University team in MADAR shared task, which addresses the problem of sentence-level fine-grained Arabic Dialect Identification over 25 different Arabic dialects in addition to the Modern Standard Arabic. Arabic Dialect Identification is not a trivial task since different dialects share some features, e.g., utilizing the same character set and some vocabularies. We opted to adopt a very simple approach in terms of extracted features and classification models; we only utilize word and character n-grams as features, and Na {\ensuremath{\ddot{}}}{\i}ve Bayes models as classifiers. Surprisingly, the simple approach achieved non-na {\ensuremath{\ddot{}}}{\i}ve performance. The official results, reported on a held-out testing set, show that the dialect of a given sentence can be identified at an accuracy of 64.58{\%} by our best submitted run.
LIUM-MIRACL Participation in the MADAR Arabic Dialect Identification Shared Task (2019)

This paper describes the joint participation of the LIUM and MIRACL Laboratories at the Arabic dialect identification challenge of the MADAR Shared Task (Bouamor et al., 2019) conducted during the Fourth Arabic Natural Language Processing Workshop (WANLP 2019). We participated to the Travel Domain Dialect Identification subtask. We built several systems and explored different techniques including conventional machine learning methods and deep learning algorithms. Deep learning approaches did not perform well on this task. We experimented several classification systems and we were able to identify the dialect of an input sentence with an F1-score of 65.41{\%} on the official test set using only the training data supplied by the shared task organizers.
Arabic Dialect Identification with Deep Learning and Hybrid Frequency Based Features (2019)

Studies on Dialectical Arabic are growing more important by the day as it becomes the primary written and spoken form of Arabic online in informal settings. Among the important problems that should be explored is that of dialect identification. This paper reports different techniques that can be applied towards such goal and reports their performance on the Multi Arabic Dialect Applications and Resources (MADAR) Arabic Dialect Corpora. Our results show that improving on traditional systems using frequency based features and non deep learning classifiers is a challenging task. We propose different models based on different word and document representations. Our top model is able to achieve an F1 macro averaged score of 65.66 on MADAR`s small-scale parallel corpus of 25 dialects and Modern Standard Arabic (MSA).
MICHAEL: Mining Character-level Patterns for Arabic Dialect Identification (MADAR Challenge) (2019)

We present MICHAEL, a simple lightweight method for automatic Arabic Dialect Identification on the MADAR travel domain Dialect Identification (DID). MICHAEL uses simple character-level features in order to perform a pre-processing free classification. More precisely, Character N-grams extracted from the original sentences are used to train a Multinomial Naive Bayes classifier. This system achieved an official score (accuracy) of 53.25{\%} with 1{\ensuremath{<}}=N{\ensuremath{<}}=3 but showed a much better result with character 4-grams (62.17{\%} accuracy).
Arabic Dialect Identification for Travel and Twitter Text (2019)

This paper presents the results of the experiments done as a part of MADAR Shared Task in WANLP 2019 on Arabic Fine-Grained Dialect Identification. Dialect Identification is one of the prominent tasks in the field of Natural language processing where the subsequent language modules can be improved based on it. We explored the use of different features like char, word n-gram, language model probabilities, etc on different classifiers. Results show that these features help to improve dialect classification accuracy. Results also show that traditional machine learning classifier tends to perform better when compared to neural network models on this task in a low resource setting.
Mawdoo3 AI at MADAR Shared Task: Arabic Tweet Dialect Identification (2019)

Arabic dialect identification is an inherently complex problem, as Arabic dialect taxonomy is convoluted and aims to dissect a continuous space rather than a discrete one. In this work, we present machine and deep learning approaches to predict 21 fine-grained dialects form a set of given tweets per user. We adopted numerous feature extraction methods most of which showed improvement in the final model, such as word embedding, Tf-idf, and other tweet features. Our results show that a simple LinearSVC can outperform any complex deep learning model given a set of curated features. With a relatively complex user voting mechanism, we were able to achieve a Macro-Averaged F1-score of 71.84{\%} on MADAR shared subtask-2. Our best submitted model ranked second out of all participating teams.
Mawdoo3 AI at MADAR Shared Task: Arabic Fine-Grained Dialect Identification with Ensemble Learning (2019)

In this paper we discuss several models we used to classify 25 city-level Arabic dialects in addition to Modern Standard Arabic (MSA) as part of MADAR shared task (sub-task 1). We propose an ensemble model of a group of experimentally designed best performing classifiers on a various set of features. Our system achieves an accuracy of 69.3{\%} macro F1-score with an improvement of 1.4{\%} accuracy from the baseline model on the DEV dataset. Our best run submitted model ranked as third out of 19 participating teams on the TEST dataset with only 0.12{\%} macro F1-score behind the top ranked system.
Hierarchical Deep Learning for Arabic Dialect Identification (2019)

In this paper, we present two approaches for Arabic Fine-Grained Dialect Identification. The first approach is based on Recurrent Neural Networks (BLSTM, BGRU) using hierarchical classification. The main idea is to separate the classification process for a sentence from a given text in two stages. We start with a higher level of classification (8 classes) and then the finer-grained classification (26 classes). The second approach is given by a voting system based on Naive Bayes and Random Forest. Our system achieves an F1 score of 63.02 {\%} on the subtask evaluation dataset.
ArbDialectID at MADAR Shared Task 1: Language Modelling and Ensemble Learning for Fine Grained Arabic Dialect Identification (2019)

In this paper, we present a Dialect Identification system (ArbDialectID) that competed at Task 1 of the MADAR shared task, MADARTravel Domain Dialect Identification. We build a course and a fine-grained identification model to predict the label (corresponding to a dialect of Arabic) of a given text. We build two language models by extracting features at two levels (words and characters). We firstly build a coarse identification model to classify each sentence into one out of six dialects, then use this label as a feature for the fine-grained model that classifies the sentence among 26 dialects from different Arab cities, after that we apply ensemble voting classifier on both sub-systems. Our system ranked 1st that achieving an f-score of 67.32{\%}. Both the models and our feature engineering tools are made available to the research community.
The SMarT Classifier for Arabic Fine-Grained Dialect Identification (2019)

This paper describes the approach adopted by the SMarT research group to build a dialect identification system in the framework of the Madar shared task on Arabic fine-grained dialect identification. We experimented several approaches, but we finally decided to use a Multinomial Naive Bayes classifier based on word and character ngrams in addition to the language model probabilities. We achieved a score of 67.73{\%} in terms of Macro accuracy and a macro-averaged F1-score of 67.31{\%}
JHU System Description for the MADAR Arabic Dialect Identification Shared Task (2019)

Our submission to the MADAR shared task on Arabic dialect identification employed a language modeling technique called Prediction by Partial Matching, an ensemble of neural architectures, and sources of additional data for training word embeddings and auxiliary language models. We found several of these techniques provided small boosts in performance, though a simple character-level language model was a strong baseline, and a lower-order LM achieved best performance on Subtask 2. Interestingly, word embeddings provided no consistent benefit, and ensembling struggled to outperform the best component submodel. This suggests the variety of architectures are learning redundant information, and future work may focus on encouraging decorrelated learning.
ST MADAR 2019 Shared Task: Arabic Fine-Grained Dialect Identification (2019)

This paper describes the solution that we propose on MADAR 2019 Arabic Fine-Grained Dialect Identification task. The proposed solution utilized a set of classifiers that we trained on character and word features. These classifiers are: Support Vector Machines (SVM), Bernoulli Naive Bayes (BNB), Multinomial Naive Bayes (MNB), Logistic Regression (LR), Stochastic Gradient Descent (SGD), Passive Aggressive(PA) and Perceptron (PC). The system achieved competitive results, with a performance of 62.87 {\%} and 62.12 {\%} for both development and test sets.
A Character Level Convolutional BiLSTM for Arabic Dialect Identification (2019)

In this paper, we describe CU-RAISA teamcontribution to the 2019Madar shared task2, which focused on Twitter User fine-grained dialect identification. Among par-ticipating teams, our system ranked the4th(with 61.54{\%}) F1-Macro measure. Our sys-tem is trained using a character level convo-lutional bidirectional long-short-term memorynetwork trained on 2k users' data. We showthat training on concatenated user tweets asinput is further superior to training on usertweets separately and assign user`s label on themode of user`s tweets' predictions.
No Army, No Navy: BERT Semi-Supervised Learning of Arabic Dialects (2019)

We present our deep leaning system submitted to MADAR shared task 2 focused on twitter user dialect identification. We develop tweet-level identification models based on GRUs and BERT in supervised and semi-supervised set-tings. We then introduce a simple, yet effective, method of porting tweet-level labels at the level of users. Our system ranks top 1 in the competition, with 71.70{\%} macro F1 score and 77.40{\%} accuracy.
Team JUST at the MADAR Shared Task on Arabic Fine-Grained Dialect Identification (2019)

In this paper, we describe our team`s effort on the MADAR Shared Task on Arabic Fine-Grained Dialect Identification. The task requires building a system capable of differentiating between 25 different Arabic dialects in addition to MSA. Our approach is simple. After preprocessing the data, we use Data Augmentation (DA) to enlarge the training data six times. We then build a language model and extract n-gram word-level and character-level TF-IDF features and feed them into an MNB classifier. Despite its simplicity, the resulting model performs really well producing the 4th highest F-measure and region-level accuracy and the 5th highest precision, recall, city-level accuracy and country-level accuracy among the participating teams.
QC-GO Submission for MADAR Shared Task: Arabic Fine-Grained Dialect Identification (2019)

This paper describes the QC-GO team submission to the MADAR Shared Task Subtask 1 (travel domain dialect identification) and Subtask 2 (Twitter user location identification). In our participation in both subtasks, we explored a number of approaches and system combinations to obtain the best performance for both tasks. These include deep neural nets and heuristics. Since individual approaches suffer from various shortcomings, the combination of different approaches was able to fill some of these gaps. Our system achieves F1-Scores of 66.1{\%} and 67.0{\%} on the development sets for Subtasks 1 and 2 respectively.
From Insanely Jealous to Insanely Delicious: Computational Models for the Semantic Bleaching of English Intensifiers (2019)

We introduce novel computational models for modeling semantic bleaching, a widespread category of change in which words become more abstract or lose elements of meaning, like the development of {\textquotedblleft}arrive{\textquotedblright} from its earlier meaning {\textquoteleft}become at shore.' We validate our methods on a widespread case of bleaching in English: de-adjectival adverbs that originate as manner adverbs (as in {\textquotedblleft}awfully behaved{\textquotedblright}) and later become intensifying adverbs (as in {\textquotedblleft}awfully nice{\textquotedblright}). Our methods formally quantify three reflexes of bleaching: decreasing similarity to the source meaning (e.g., {\textquotedblleft}awful{\textquotedblright}), increasing similarity to a fully bleached prototype (e.g., {\textquotedblleft}very{\textquotedblright}), and increasing productivity (e.g., the breadth of adjectives that an adverb modifies). We also test a new causal model and find evidence that bleaching is initially triggered in contexts such as {\textquotedblleft}conspicuously evident{\textquotedblright} and {\textquotedblleft}insanely jealous{\textquotedblright}, where an adverb premodifies a semantically similar adjective. These contexts provide a form of {\textquotedblleft}bridging context{\textquotedblright} (Evans and Wilkins, 2000) that allow a manner adverb to be reinterpreted as an intensifying adverb similar to {\textquotedblleft}very{\textquotedblright}.
Computational Analysis of the Historical Changes in Poetry and Prose (2019)

The esoteric definitions of poetry are insufficient in enveloping the changes in poetry that the age of mechanical reproduction has witnessed with the widespread proliferation of the use of digital media and artificial intelligence. They are also insufficient in distinguishing between prose and poetry, as the content of both prose and poetry can be poetic. Using quotes as prose considering their poetic, context-free and celebrated nature, stylistic differences between poetry and prose are delved into. Novel features in grammar and meter are justified as distinguishing features. Datasets of popular prose and poetry spanning across 1870-1920 and 1970-2019 have been created, and multiple experiments have been conducted to prove that prose and poetry in the latter period are more alike than they were in the former. The accuracy of classification of poetry and prose of 1970-2019 is significantly lesser than that of 1870-1920, thereby proving the convergence of poetry and prose in 1970-2019.
Studying Semantic Chain Shifts with Word2Vec: FOOD\ensuremath>MEAT\ensuremath>FLESH (2019)

Word2Vec models are used to study the semantic chain shift FOOD{\ensuremath{>}}MEAT{\ensuremath{>}}FLESH in the history of English, c. 1425-1925. The development stretches out over a long time, starting before 1500, and may possibly be continuing to this day. The semantic changes likely proceeded as a push chain.
Evaluation of Semantic Change of Harm-Related Concepts in Psychology (2019)

The paper focuses on diachronic evaluation of semantic changes of harm-related concepts in psychology. More specifically, we investigate a hypothesis that certain concepts such as {\textquotedblleft}addiction{\textquotedblright}, {\textquotedblleft}bullying{\textquotedblright}, {\textquotedblleft}harassment{\textquotedblright}, {\textquotedblleft}prejudice{\textquotedblright}, and {\textquotedblleft}trauma{\textquotedblright} became broader during the last four decades. We evaluate semantic changes using two models: an LSA-based model from Sagi et al. (2009) and a diachronic adaptation of word2vec from Hamilton et al. (2016), that are trained on a large corpus of journal abstracts covering the period of 1980{--} 2019. Several concepts showed evidence of broadening. {\textquotedblleft}Addiction{\textquotedblright} moved from physiological dependency on a substance to include psychological dependency on gaming and the Internet. Similarly, {\textquotedblleft}harassment{\textquotedblright} and {\textquotedblleft}trauma{\textquotedblright} shifted towards more psychological meanings. On the other hand, {\textquotedblleft}bullying{\textquotedblright} has transformed into a more victim-related concept and expanded to new areas such as workplaces.
Contextualized Diachronic Word Representations (2019)

Diachronic word embeddings play a key role in capturing interesting patterns about how language evolves over time. Most of the existing work focuses on studying corpora spanning across several decades, which is understandably still not a possibility when working on social media-based user-generated content. In this work, we address the problem of studying semantic changes in a large Twitter corpus collected over five years, a much shorter period than what is usually the norm in diachronic studies. We devise a novel attentional model, based on Bernoulli word embeddings, that are conditioned on contextual extra-linguistic (social) features such as network, spatial and socio-economic variables, which are associated with Twitter users, as well as topic-based features. We posit that these social features provide an inductive bias that helps our model to overcome the narrow time-span regime problem. Our extensive experiments reveal that our proposed model is able to capture subtle semantic shifts without being biased towards frequency cues and also works well when certain contextual features are absent. Our model fits the data better than current state-of-the-art dynamic word embedding models and therefore is a promising tool to study diachronic semantic changes over small time periods.
Semantic Change and Semantic Stability: Variation is Key (2019)

I survey some recent approaches to studying change in the lexicon, particularly change in meaning across phylogenies. I briefly sketch an evolutionary approach to language change and point out some issues in recent approaches to studying semantic change that rely on temporally stratified word embeddings. I draw illustrations from lexical cognate models in Pama-Nyungan to identify meaning classes most appropriate for lexical phylogenetic inference, particularly highlighting the importance of variation in studying change over time.
GASC: Genre-Aware Semantic Change for Ancient Greek (2019)

Word meaning changes over time, depending on linguistic and extra-linguistic factors. Associating a word`s correct meaning in its historical context is a central challenge in diachronic research, and is relevant to a range of NLP tasks, including information retrieval and semantic search in historical texts. Bayesian models for semantic change have emerged as a powerful tool to address this challenge, providing explicit and interpretable representations of semantic change phenomena. However, while corpora typically come with rich metadata, existing models are limited by their inability to exploit contextual information (such as text genre) beyond the document time-stamp. This is particularly critical in the case of ancient languages, where lack of data and long diachronic span make it harder to draw a clear distinction between polysemy (the fact that a word has several senses) and semantic change (the process of acquiring, losing, or changing senses), and current systems perform poorly on these languages. We develop GASC, a dynamic semantic change model that leverages categorical metadata about the texts' genre to boost inference and uncover the evolution of meanings in Ancient Greek corpora. In a new evaluation framework, our model achieves improved predictive performance compared to the state of the art.
Modeling Markedness with a Split-and-Merger Model of Sound Change (2019)

The concept of {\textquoteleft}markedness' has been influential in phonology for almost a century. Theoretical phonology has found it useful to describe some segments as more {\textquoteleft}marked' than others, referring to a cluster of language-internal and -external properties (Jakobson 1968, Haspelmath 2006). We argue, using a simple mathematical model based on Evolutionary Phonology (Blevins 2004), that markedness is an epiphenomenon of phonetically grounded sound change.
A Method to Automatically Identify Diachronic Variation in Collocations. (2019)

This paper introduces a novel method to track collocational variations in diachronic corpora that can identify several changes undergone by these phraseological combinations and to propose alternative solutions found in later periods. The strategy consists of extracting syntactically-related candidates of collocations and ranking them using statistical association measures. Then, starting from the first period of the corpus, the system tracks each combination over time, verifying different types of historical variation such as the loss of one or both lemmas, the disappearance of the collocation, or its diachronic frequency trend. Using a distributional semantics strategy, it also proposes other linguistic structures which convey similar meanings to those extinct collocations. A case study on historical corpora of Portuguese and Spanish shows that the system speeds up and facilitates the finding of some diachronic changes and phraseological shifts that are harder to identify without using automated methods.
Written on Leaves or in Stones?: Computational Evidence for the Era of Authorship of Old Thai Prose (2019)

We aim to provide computational evidence for the era of authorship of two important old Thai texts: Traiphumikatha and Pumratchatham. The era of authorship of these two books is still an ongoing debate among Thai literature scholars. Analysis of old Thai texts present a challenge for standard natural language processing techniques, due to the lack of corpora necessary for building old Thai word and syllable segmentation. We propose an accurate and interpretable model to classify each segment as one of the three eras of authorship (Sukhothai, Ayuddhya, or Rattanakosin) without sophisticated linguistic preprocessing. Contrary to previous hypotheses, our model suggests that both books were written during the Sukhothai era. Moreover, the second half of the Pumratchtham is uncharacteristic of the Sukhothai era, which may have confounded literary scholars in the past. Further, our model reveals that the most indicative linguistic changes stem from unidirectional grammaticalized words and polyfunctional words, which show up as most dominant features in the model.
Identifying Temporal Trends Based on Perplexity and Clustering: Are We Looking at Language Change? (2019)

In this work we propose a data-driven methodology for identifying temporal trends in a corpus of medieval charters. We have used perplexities derived from RNNs as a distance measure between documents and then, performed clustering on those distances. We argue that perplexities calculated by such language models are representative of temporal trends. The clusters produced using the K-Means algorithm give an insight of the differences in language in different time periods at least partly due to language change. We suggest that the temporal distribution of the individual clusters might provide a more nuanced picture of temporal trends compared to discrete bins, thus providing better results when used in a classification task.