pwc-archive/papers-with-abstracts huggingface dataset:
Discriminating between Similar Languages with Word-level Convolutional Neural Networks
Authors: Marcelo Criscuolo, S Alu{\'\i}sio, ra Maria
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Language Identification, Question Answering, Text Classification

Discriminating between Similar Languages (DSL) is a challenging task addressed at the VarDial Workshop series. We report on our participation in the DSL shared task with a two-stage system. In the first stage, character n-grams are used to separate language groups, then specialized classifiers distinguish similar language varieties. We have conducted experiments with three system configurations and submitted one run for each. Our main approach is a word-level convolutional neural network (CNN) that learns task-specific vectors with minimal text preprocessing. We also experiment with multi-layer perceptron (MLP) networks and another hybrid configuration. Our best run achieved an accuracy of 90.76{\%}, ranking 8th among 11 participants and getting very close to the system that ranked first (less than 2 points). Even though the CNN model could not achieve the best results, it still makes a viable approach to discriminating between similar languages.
Cross-lingual dependency parsing for closely related languages - Helsinki's submission to VarDial 2017
Authors: J{\"o}rg Tiedemann
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Dependency Parsing, Machine Translation, Transfer Learning, Translation

This paper describes the submission from the University of Helsinki to the shared task on cross-lingual dependency parsing at VarDial 2017. We present work on annotation projection and treebank translation that gave good results for all three target languages in the test set. In particular, Slovak seems to work well with information coming from the Czech treebank, which is in line with related work. The attachment scores for cross-lingual models even surpass the fully supervised models trained on the target language treebank. Croatian is the most difficult language in the test set and the improvements over the baseline are rather modest. Norwegian works best with information coming from Swedish whereas Danish contributes surprisingly little.
Discriminating between Similar Languages Using a Combination of Typed and Untyped Character N-grams and Words
Authors: Helena Gomez, Ilia Markov, Jorge Baptista, Grigori Sidorov, David Pinto
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: General Classification, Information Retrieval, Machine Translation

This paper presents the cic{\_}ualg{'}s system that took part in the Discriminating between Similar Languages (DSL) shared task, held at the VarDial 2017 Workshop. This year{'}s task aims at identifying 14 languages across 6 language groups using a corpus of excerpts of journalistic texts. Two classification approaches were compared: a single-step (all languages) approach and a two-step (language group and then languages within the group) approach. Features exploited include lexical features (unigrams of words) and character n-grams. Besides traditional (untyped) character n-grams, we introduce typed character n-grams in the DSL task. Experiments were carried out with different feature representation methods (binary and raw term frequency), frequency threshold values, and machine-learning algorithms {--} Support Vector Machines (SVM) and Multinomial Naive Bayes (MNB). Our best run in the DSL task achieved 91.46{\%} accuracy.
T\"ubingen system in VarDial 2017 shared task: experiments with language identification and cross-lingual parsing
Authors: {\c{C}}a{\u{g}}r{\i} {\c{C}}{\"o}ltekin, Taraka Rama
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Dependency Parsing, Language Identification, Translation

This paper describes our systems and results on VarDial 2017 shared tasks. Besides three language/dialect discrimination tasks, we also participated in the cross-lingual dependency parsing (CLP) task using a simple methodology which we also briefly describe in this paper. For all the discrimination tasks, we used linear SVMs with character and word features. The system achieves competitive results among other systems in the shared task. We also report additional experiments with neural network models. The performance of neural network models was close but always below the corresponding SVM classifiers in the discrimination tasks. For the cross-lingual parsing task, we experimented with an approach based on automatically translating the source treebank to the target language, and training a parser on the translated treebank. We used off-the-shelf tools for both translation and parsing. Despite achieving better-than-baseline results, our scores in CLP tasks were substantially lower than the scores of the other participants.
When Sparse Traditional Models Outperform Dense Neural Networks: the Curious Case of Discriminating between Similar Languages
Authors: Maria Medvedeva, Martin Kroon, Barbara Plank
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Language Identification

We present the results of our participation in the VarDial 4 shared task on discriminating closely related languages. Our submission includes simple traditional models using linear support vector machines (SVMs) and a neural network (NN). The main idea was to leverage language group information. We did so with a two-layer approach in the traditional model and a multi-task objective in the neural network case. Our results confirm earlier findings: simple traditional models outperform neural networks consistently for this task, at least given the amount of systems we could examine in the available time. Our two-layer linear SVM ranked 2nd in the shared task.
German Dialect Identification in Interview Transcriptions
Authors: Shervin Malmasi, Marcos Zampieri
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Dialect Identification, Machine Translation

This paper presents three systems submitted to the German Dialect Identification (GDI) task at the VarDial Evaluation Campaign 2017. The task consists of training models to identify the dialect of Swiss-German speech transcripts. The dialects included in the GDI dataset are Basel, Bern, Lucerne, and Zurich. The three systems we submitted are based on: a plurality ensemble, a mean probability ensemble, and a meta-classifier trained on character and word n-grams. The best results were obtained by the meta-classifier achieving 68.1{\%} accuracy and 66.2{\%} F1-score, ranking first among the 10 teams which participated in the GDI shared task.
CLUZH at VarDial GDI 2017: Testing a Variety of Machine Learning Tools for the Classification of Swiss German Dialects
Authors: Simon Clematide, Peter Makarov
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: General Classification, Language Identification, Text Classification

Our submissions for the GDI 2017 Shared Task are the results from three different types of classifiers: Na{\"\i}ve Bayes, Conditional Random Fields (CRF), and Support Vector Machine (SVM). Our CRF-based run achieves a weighted F1 score of 65{\%} (third rank) being beaten by the best system by 0.9{\%}. Measured by classification accuracy, our ensemble run (Na{\"\i}ve Bayes, CRF, SVM) reaches 67{\%} (second rank) being 1{\%} lower than the best system. We also describe our experiments with Recurrent Neural Network (RNN) architectures. Since they performed worse than our non-neural approaches we did not include them in the submission.
Arabic Dialect Identification Using iVectors and ASR Transcripts
Authors: Shervin Malmasi, Marcos Zampieri
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Dialect Identification, Machine Translation

This paper presents the systems submitted by the MAZA team to the Arabic Dialect Identification (ADI) shared task at the VarDial Evaluation Campaign 2017. The goal of the task is to evaluate computational models to identify the dialect of Arabic utterances using both audio and text transcriptions. The ADI shared task dataset included Modern Standard Arabic (MSA) and four Arabic dialects: Egyptian, Gulf, Levantine, and North-African. The three systems submitted by MAZA are based on combinations of multiple machine learning classifiers arranged as (1) voting ensemble; (2) mean probability ensemble; (3) meta-classifier. The best results were obtained by the meta-classifier achieving 71.7{\%} accuracy, ranking second among the six teams which participated in the ADI shared task.
Discriminating between Similar Languages using Weighted Subword Features
Authors: Adrien Barbaresi
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Language Identification, Text Categorization

The present contribution revolves around a contrastive subword n-gram model which has been tested in the Discriminating between Similar Languages shared task. I present and discuss the method used in this 14-way language identification task comprising varieties of 6 main language groups. It features the following characteristics: (1) the preprocessing and conversion of a collection of documents to sparse features; (2) weighted character n-gram profiles; (3) a multinomial Bayesian classifier. Meaningful bag-of-n-grams features can be used as a system in a straightforward way, my approach outperforms most of the systems used in the DSL shared task (3rd rank).
Exploring Lexical and Syntactic Features for Language Variety Identification
Authors: Chris van der Lee, Antal Van den Bosch
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: BIG-bench Machine Learning, Language Identification, Sentence, Text Classification

We present a method to discriminate between texts written in either the Netherlandic or the Flemish variant of the Dutch language. The method draws on a feature bundle representing text statistics, syntactic features, and word $n$-grams. Text statistics include average word length and sentence length, while syntactic features include ratios of function words and part-of-speech $n$-grams. The effectiveness of the classifier was measured by classifying Dutch subtitles developed for either Dutch or Flemish television. Several machine learning algorithms were compared as well as feature combination methods in order to find the optimal generalization performance. A machine-learning meta classifier based on AdaBoost attained the best F-score of 0.92.
Learning to Identify Arabic and German Dialects using Multiple Kernels
Authors: Radu Tudor Ionescu, Andrei Butnaru
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Dialect Identification

We present a machine learning approach for the Arabic Dialect Identification (ADI) and the German Dialect Identification (GDI) Closed Shared Tasks of the DSL 2017 Challenge. The proposed approach combines several kernels using multiple kernel learning. While most of our kernels are based on character p-grams (also known as n-grams) extracted from speech transcripts, we also use a kernel based on i-vectors, a low-dimensional representation of audio recordings, provided only for the Arabic data. In the learning stage, we independently employ Kernel Discriminant Analysis (KDA) and Kernel Ridge Regression (KRR). Our approach is shallow and simple, but the empirical results obtained in the shared tasks prove that it achieves very good results. Indeed, we ranked on the first place in the ADI Shared Task with a weighted F1 score of 76.32{\%} (4.62{\%} above the second place) and on the fifth place in the GDI Shared Task with a weighted F1 score of 63.67{\%} (2.57{\%} below the first place).
Slavic Forest, Norwegian Wood
Authors: Rudolf Rosa, Daniel Zeman, David Mare{\v{c}}ek, Zden{\v{e}}k {\v{Z}}abokrtsk{\'y}
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Dependency Parsing, Machine Translation, Word Alignment

We once had a corp, or should we say, it once had us They showed us its tags, isn{'}t it great, unified tags They asked us to parse and they told us to use everything So we looked around and we noticed there was near nothing We took other langs, bitext aligned: words one-to-one We played for two weeks, and then they said, here is the test The parser kept training till morning, just until deadline So we had to wait and hope what we get would be just fine And, when we awoke, the results were done, we saw we{'}d won So, we wrote this paper, isn{'}t it good, Norwegian wood.
Identification of Languages in Algerian Arabic Multilingual Documents
Authors: Wafia Adouane, Simon Dobnik
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Chunking, General Classification, Language Identification

This paper presents a language identification system designed to detect the language of each word, in its context, in a multilingual documents as generated in social media by bilingual/multilingual communities, in our case speakers of Algerian Arabic. We frame the task as a sequence tagging problem and use supervised machine learning with standard methods like HMM and Ngram classification tagging. We also experiment with a lexicon-based method. Combining all the methods in a fall-back mechanism and introducing some linguistic rules, to deal with unseen tokens and ambiguous words, gives an overall accuracy of 93.14{\%}. Finally, we introduced rules for language identification from sequences of recognised words.
Arabic Diacritization: Stats, Rules, and Hacks
Authors: Kareem Darwish, Hamdy Mubarak, Ahmed Abdelali
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Decoder, Part-Of-Speech Tagging, Transliteration, Word Sense Disambiguation

In this paper, we present a new and fast state-of-the-art Arabic diacritizer that guesses the diacritics of words and then their case endings. We employ a Viterbi decoder at word-level with back-off to stem, morphological patterns, and transliteration and sequence labeling based diacritization of named entities. For case endings, we use Support Vector Machine (SVM) based ranking coupled with morphological patterns and linguistic rules to properly guess case endings. We achieve a low word level diacritization error of 3.29{\%} and 12.77{\%} without and with case endings respectively on a new multi-genre free of copyright test set. We are making the diacritizer available for free for research purposes.
Semantic Similarity of Arabic Sentences with Word Embeddings
Authors: El Moatez Billah Nagoudi, Didier Schwab
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Descriptive, Information Retrieval, Machine Translation, Part-Of-Speech Tagging, Retrieval, Semantic Similarity, Semantic Textual Similarity, Sentence, Text Classification, Text Summarization, Translation, Word Embeddings

Semantic textual similarity is the basis of countless applications and plays an important role in diverse areas, such as information retrieval, plagiarism detection, information extraction and machine translation. This article proposes an innovative word embedding-based system devoted to calculate the semantic similarity in Arabic sentences. The main idea is to exploit vectors as word representations in a multidimensional space in order to capture the semantic and syntactic properties of words. IDF weighting and Part-of-Speech tagging are applied on the examined sentences to support the identification of words that are highly descriptive in each sentence. The performance of our proposed system is confirmed through the Pearson correlation between our assigned semantic similarity scores and human judgments.
A Morphological Analyzer for Gulf Arabic Verbs
Authors: Salam Khalifa, Sara Hassan, Nizar Habash
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Morphological Tagging, Part-Of-Speech Tagging, POS, TAG

We present CALIMAGLF, a Gulf Arabic morphological analyzer currently covering over 2,600 verbal lemmas. We describe in detail the process of building the analyzer starting from phonetic dictionary entries to fully inflected orthographic paradigms and associated lexicon and orthographic variants. We evaluate the coverage of CALIMA-GLF against Modern Standard Arabic and Egyptian Arabic analyzers on part of a Gulf Arabic novel. CALIMA-GLF verb analysis token recall for identifying correct POS tag outperforms both the Modern Standard Arabic and Egyptian Arabic analyzers by over 27.4{\%} and 16.9{\%} absolute, respectively.
A Neural Architecture for Dialectal Arabic Segmentation
Authors: Younes Samih, Mohammed Attia, Mohamed Eldesouki, Ahmed Abdelali, Hamdy Mubarak, Laura Kallmeyer, Kareem Darwish
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Machine Translation, Morphological Analysis, Part-Of-Speech Tagging, Segmentation

The automated processing of Arabic Dialects is challenging due to the lack of spelling standards and to the scarcity of annotated data and resources in general. Segmentation of words into its constituent parts is an important processing building block. In this paper, we show how a segmenter can be trained using only 350 annotated tweets using neural networks without any normalization or use of lexical features or lexical resources. We deal with segmentation as a sequence labeling problem at the character level. We show experimentally that our model can rival state-of-the-art methods that rely on additional resources.
Sentiment Analysis of Tunisian Dialects: Linguistic Ressources and Experiments
Authors: Salima Medhaffar, Fethi Bougares, Yannick Est{\`e}ve, Lamia Hadrich-Belguith
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Sentiment Analysis

Dialectal Arabic (DA) is significantly different from the Arabic language taught in schools and used in written communication and formal speech (broadcast news, religion, politics, etc.). There are many existing researches in the field of Arabic language Sentiment Analysis (SA); however, they are generally restricted to Modern Standard Arabic (MSA) or some dialects of economic or political interest. In this paper we are interested in the SA of the Tunisian Dialect. We utilize Machine Learning techniques to determine the polarity of comments written in Tunisian Dialect. First, we evaluate the SA systems performances with models trained using freely available MSA and Multi-dialectal data sets. We then collect and annotate a Tunisian Dialect corpus of 17.000 comments from Facebook. This corpus allows us a significant accuracy improvement compared to the best model trained on other Arabic dialects or MSA data. We believe that this first freely available corpus will be valuable to researchers working in the field of Tunisian Sentiment Analysis and similar areas.
CAT: Credibility Analysis of Arabic Content on Twitter
Authors: Rim El Ballouli, Wassim El-Hajj, Gh, Ahmad our, Shady Elbassuoni, Hazem Hajj, Khaled Shaban
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Emotion Recognition, Opinion Mining, Sentiment Analysis

Data generated on Twitter has become a rich source for various data mining tasks. Those data analysis tasks that are dependent on the tweet semantics, such as sentiment analysis, emotion mining, and rumor detection among others, suffer considerably if the tweet is not credible, not real, or spam. In this paper, we perform an extensive analysis on credibility of Arabic content on Twitter. We also build a classification model (CAT) to automatically predict the credibility of a given Arabic tweet. Of particular originality is the inclusion of features extracted directly or indirectly from the author{'}s profile and timeline. To train and test CAT, we annotated for credibility a data set of 9,000 Arabic tweets that are topic independent. CAT achieved consistent improvements in predicting the credibility of the tweets when compared to several baselines and when compared to the state-of-the-art approach with an improvement of 21{\%} in weighted average F-measure. We also conducted experiments to highlight the importance of the user-based features as opposed to the content-based features. We conclude our work with a feature reduction experiment that highlights the best indicative features of credibility.
A New Error Annotation for Dyslexic texts in Arabic
Authors: Maha Alamri, William J. Teahan
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Classification, General Classification

This paper aims to develop a new classification of errors made in Arabic by those suffering from dyslexia to be used in the annotation of the Arabic dyslexia corpus (BDAC). The dyslexic error classification for Arabic texts (DECA) comprises a list of spelling errors extracted from previous studies and a collection of texts written by people with dyslexia that can provide a framework to help analyse specific errors committed by dyslexic writers. The classification comprises 37 types of errors, grouped into nine categories. The paper also discusses building a corpus of dyslexic Arabic texts that uses the error annotation scheme and provides an analysis of the errors that were found in the texts.
An Unsupervised Speaker Clustering Technique based on SOM and I-vectors for Speech Recognition Systems
Authors: Hany Ahmed, Mohamed Elaraby, Abdullah M. Mousa, Mostafa Elhosiny, Sherif Abdou, Mohsen Rashwan
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Automatic Speech Recognition (ASR), Clustering, Speaker Identification, speech-recognition, Speech Recognition

In this paper, we introduce an enhancement for speech recognition systems using an unsupervised speaker clustering technique. The proposed technique is mainly based on I-vectors and Self-Organizing Map Neural Network(SOM).The input to the proposed algorithm is a set of speech utterances. For each utterance, we extract 100-dimensional I-vector and then SOM is used to group the utterances to different speakers. In our experiments, we compared our technique with Normalized Cross Likelihood ratio Clustering (NCLR). Results show that the proposed technique reduces the speaker error rate in comparison with NCLR. Finally, we have experimented the effect of speaker clustering on Speaker Adaptive Training (SAT) in a speech recognition system implemented to test the performance of the proposed technique. It was noted that the proposed technique reduced the WER over clustering speakers with NCLR.
SHAKKIL: An Automatic Diacritization System for Modern Standard Arabic Texts
Authors: Amany Fashwan, Sameh Alansary
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Information Retrieval, Machine Translation, Morphological Disambiguation

This paper sheds light on a system that would be able to diacritize Arabic texts automatically (SHAKKIL). In this system, the diacritization problem will be handled through two levels; morphological and syntactic processing levels. The adopted morphological disambiguation algorithm depends on four layers; Uni-morphological form layer, rule-based morphological disambiguation layer, statistical-based disambiguation layer and Out Of Vocabulary (OOV) layer. The adopted syntactic disambiguation algorithms is concerned with detecting the case ending diacritics depending on a rule based approach simulating the shallow parsing technique. This will be achieved using an annotated corpus for extracting the Arabic linguistic rules, building the language models and testing the system output. This system is considered as a good trial of the interaction between rule-based approach and statistical approach, where the rules can help the statistics in detecting the right diacritization and vice versa. At this point, the morphological Word Error Rate (WER) is 4.56{\%} while the morphological Diacritic Error Rate (DER) is 1.88{\%} and the syntactic WER is 9.36{\%}. The best WER is 14.78{\%} compared to the best-published results, of (Abandah, 2015); 11.68{\%}, (Rashwan, et al., 2015); 12.90{\%} and (Metwally, Rashwan, {\&} Atiya, 2016); 13.70{\%}.