pwc-archive/papers-with-abstracts huggingface dataset:
LSTM Autoencoders for Dialect Analysis
Authors: Taraka Rama, {\c{C}}a{\u{g}}r{\i} {\c{C}}{\"o}ltekin
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Dimensionality Reduction

Computational approaches for dialectometry employed Levenshtein distance to compute an aggregate similarity between two dialects belonging to a single language group. In this paper, we apply a sequence-to-sequence autoencoder to learn a deep representation for words that can be used for meaningful comparison across dialects. In contrast to the alignment-based methods, our method does not require explicit alignments. We apply our architectures to three different datasets and show that the learned representations indicate highly similar results with the analyses based on Levenshtein distance and capture the traditional dialectal differences shown by dialectologists.
The GW/LT3 VarDial 2016 Shared Task System for Dialects and Similar Languages Detection
Authors: Ayah Zirikly, Bart Desmet, Mona Diab
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Feature Engineering, regression, Task 2

This paper describes the GW/LT3 contribution to the 2016 VarDial shared task on the identification of similar languages (task 1) and Arabic dialects (task 2). For both tasks, we experimented with Logistic Regression and Neural Network classifiers in isolation. Additionally, we implemented a cascaded classifier that consists of coarse and fine-grained classifiers (task 1) and a classifier ensemble with majority voting for task 2. The submitted systems obtained state-of-the art performance and ranked first for the evaluation on social media data (test sets B1 and B2 for task 1), with a maximum weighted F1 score of 91.94{\%}.
Processing Dialectal Arabic: Exploiting Variability and Similarity to Overcome Challenges and Discover Opportunities
Authors: Mona Diab
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Machine Translation

We recently witnessed an exponential growth in dialectal Arabic usage in both textual data and speech recordings especially in social media. Processing such media is of great utility for all kinds of applications ranging from information extraction to social media analytics for political and commercial purposes to building decision support systems. Compared to other languages, Arabic, especially the informal variety, poses a significant challenge to natural language processing algorithms since it comprises multiple dialects, linguistic code switching, and a lack of standardized orthographies, to top its relatively complex morphology. Inherently, the problem of processing Arabic in the context of social media is the problem of how to handle resource poor languages. In this talk I will go over some of our insights to some of these problems and show how there is a silver lining where we can generalize some of our solutions to other low resource language contexts.
Language Related Issues for Machine Translation between Closely Related South Slavic Languages
Authors: Maja Popovi{\'c}, Mihael Ar{\v{c}}an, Filip Klubi{\v{c}}ka
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Machine Translation, Translation

Machine translation between closely related languages is less challenging and exibits a smaller number of translation errors than translation between distant languages, but there are still obstacles which should be addressed in order to improve such systems. This work explores the obstacles for machine translation systems between closely related South Slavic languages, namely Croatian, Serbian and Slovenian. Statistical systems for all language pairs and translation directions are trained using parallel texts from different domains, however mainly on spoken language i.e. subtitles. For translation between Serbian and Croatian, a rule-based system is also explored. It is shown that for all language pairs and translation systems, the main obstacles are differences between structural properties.
Romanized Berber and Romanized Arabic Automatic Language Identification Using Machine Learning
Authors: Wafia Adouane, Nasredine Semmar, Richard Johansson
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: BIG-bench Machine Learning, Language Identification, Transliteration

The identification of the language of text/speech input is the first step to be able to properly do any language-dependent natural language processing. The task is called Automatic Language Identification (ALI). Being a well-studied field since early 1960{'}s, various methods have been applied to many standard languages. The ALI standard methods require datasets for training and use character/word-based n-gram models. However, social media and new technologies have contributed to the rise of informal and minority languages on the Web. The state-of-the-art automatic language identifiers fail to properly identify many of them. Romanized Arabic (RA) and Romanized Berber (RB) are cases of these informal languages which are under-resourced. The goal of this paper is twofold: detect RA and RB, at a document level, as separate languages and distinguish between them as they coexist in North Africa. We consider the task as a classification problem and use supervised machine learning to solve it. For both languages, character-based 5-grams combined with additional lexicons score the best, F-score of 99.75{\%} and 97.77{\%} for RB and RA respectively.
How Many Languages Can a Language Model Model?
Authors: Robert {\"O}stling
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Language Modeling, Language Modelling, Machine Translation, model

One of the purposes of the VarDial workshop series is to encourage research into NLP methods that treat human languages as a continuum, by designing models that exploit the similarities between languages and variants. In my work, I am using a continuous vector representation of languages that allows modeling and exploring the language continuum in a very direct way. The basic tool for this is a character-based recurrent neural network language model conditioned on language vectors whose values are learned during training. By feeding the model Bible translations in a thousand languages, not only does the learned vector space capture language similarity, but by interpolating between the learned vectors it is possible to generate text in unattested intermediate forms between the training languages.
Automatic Detection of Arabicized Berber and Arabic Varieties
Authors: Wafia Adouane, Nasredine Semmar, Richard Johansson, Victoria Bobicev
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Language Identification

Automatic Language Identification (ALI) is the detection of the natural language of an input text by a machine. It is the first necessary step to do any language-dependent natural language processing task. Various methods have been successfully applied to a wide range of languages, and the state-of-the-art automatic language identifiers are mainly based on character n-gram models trained on huge corpora. However, there are many languages which are not yet automatically processed, for instance minority and informal languages. Many of these languages are only spoken and do not exist in a written format. Social media platforms and new technologies have facilitated the emergence of written format for these spoken languages based on pronunciation. The latter are not well represented on the Web, commonly referred to as under-resourced languages, and the current available ALI tools fail to properly recognize them. In this paper, we revisit the problem of ALI with the focus on Arabicized Berber and dialectal Arabic short texts. We introduce new resources and evaluate the existing methods. The results show that machine learning models combined with lexicons are well suited for detecting Arabicized Berber and different Arabic varieties and distinguishing between them, giving a macro-average F-score of 92.94{\%}.
Automatic Verification and Augmentation of Multilingual Lexicons
Authors: Maryam Aminian, Mohamed Al-Badrashiny, Mona Diab
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12

We present an approach for automatic verification and augmentation of multilingual lexica. We exploit existing parallel and monolingual corpora to extract multilingual correspondents via tri-angulation. We demonstrate the efficacy of our approach on two publicly available resources: Tharwa, a three-way lexicon comprising Dialectal Arabic, Modern Standard Arabic and English lemmas among other information (Diab et al., 2014); and BabelNet, a multilingual thesaurus comprising over 276 languages including Arabic variant entries (Navigli and Ponzetto, 2012). Our automated approach yields an F1-score of 71.71{\%} in generating correct multilingual correspondents against gold Tharwa, and 54.46{\%} against gold BabelNet without any human intervention.
Subdialectal Differences in Sorani Kurdish
Authors: Shervin Malmasi
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: General Classification, Information Retrieval, Language Identification, Machine Translation

In this study we apply classification methods for detecting subdialectal differences in Sorani Kurdish texts produced in different regions, namely Iran and Iraq. As Sorani is a low-resource language, no corpus including texts from different regions was readily available. To this end, we identified data sources that could be leveraged for this task to create a dataset of 200,000 sentences. Using surface features, we attempted to classify Sorani subdialects, showing that sentences from news sources in Iraq and Iran are distinguishable with 96{\%} accuracy. This is the first preliminary study for a dialect that has not been widely studied in computational linguistics, evidencing the possible existence of distinct subdialects.
Enlarging Scarce In-domain English-Croatian Corpus for SMT of MOOCs Using Serbian
Authors: Maja Popovi{\'c}, Kostadin Cholakov, Valia Kordoni, Nikola Ljube{\v{s}}i{\'c}
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Machine Translation, Translation

Massive Open Online Courses have been growing rapidly in size and impact. Yet the language barrier constitutes a major growth impediment in reaching out all people and educating all citizens. A vast majority of educational material is available only in English, and state-of-the-art machine translation systems still have not been tailored for this peculiar genre. In addition, a mere collection of appropriate in-domain training material is a challenging task. In this work, we investigate statistical machine translation of lecture subtitles from English into Croatian, which is morphologically rich and generally weakly supported, especially for the educational domain. We show that results comparable with publicly available systems trained on much larger data can be achieved if a small in-domain training set is used in combination with additional in-domain corpus originating from the closely related Serbian language.
Arabic Dialect Identification in Speech Transcripts
Authors: Shervin Malmasi, Marcos Zampieri
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Dialect Identification, Machine Translation

In this paper we describe a system developed to identify a set of four regional Arabic dialects (Egyptian, Gulf, Levantine, North African) and Modern Standard Arabic (MSA) in a transcribed speech corpus. We competed under the team name MAZA in the Arabic Dialect Identification sub-task of the 2016 Discriminating between Similar Languages (DSL) shared task. Our system achieved an F1-score of 0.51 in the closed training track, ranking first among the 18 teams that participated in the sub-task. Our system utilizes a classifier ensemble with a set of linear models as base classifiers. We experimented with three different ensemble fusion strategies, with the mean probability approach providing the best performance.
DSL Shared Task 2016: Perfect Is The Enemy of Good Language Discrimination Through Expectation--Maximization and Chunk-based Language Model
Authors: Ond{\v{r}}ej Herman, V{\'\i}t Suchomel, V{\'\i}t Baisa, Pavel Rychl{\'y}
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Language Modeling, Language Modelling

In this paper we investigate two approaches to discrimination of similar languages: Expectation{--}maximization algorithm for estimating conditional probability P(word|language) and byte level language models similar to compression-based language modelling methods. The accuracy of these methods reached respectively 86.6{\%} and 88.3{\%} on set A of the DSL Shared task 2016 competition.
Classifying ASR Transcriptions According to Arabic Dialect
Authors: Abualsoud Hanani, Aziz Qaroush, Stephen Taylor
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Language Modeling, Language Modelling

We describe several systems for identifying short samples of Arabic dialects. The systems were prepared for the shared task of the 2016 DSL Workshop. Our best system, an SVM using character tri-gram features, achieved an accuracy on the test data for the task of 0.4279, compared to a baseline of 0.20 for chance guesses or 0.2279 if we had always chosen the same most frequent class in the test set. This compares with the results of the team with the best weighted F1 score, which was an accuracy of 0.5117. The team entries seem to fall into cohorts, with all the teams in a cohort within a standard-deviation of each other, and our three entries are in the third cohort, which is about seven standard deviations from the top.
UnibucKernel: An Approach for Arabic Dialect Identification Based on Multiple String Kernels
Authors: Radu Tudor Ionescu, Marius Popescu
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Dialect Identification, Text Categorization

The most common approach in text mining classification tasks is to rely on features like words, part-of-speech tags, stems, or some other high-level linguistic features. Unlike the common approach, we present a method that uses only character p-grams (also known as n-grams) as features for the Arabic Dialect Identification (ADI) Closed Shared Task of the DSL 2016 Challenge. The proposed approach combines several string kernels using multiple kernel learning. In the learning stage, we try both Kernel Discriminant Analysis (KDA) and Kernel Ridge Regression (KRR), and we choose KDA as it gives better results in a 10-fold cross-validation carried out on the training set. Our approach is shallow and simple, but the empirical results obtained in the ADI Shared Task prove that it achieves very good results. Indeed, we ranked on the second place with an accuracy of 50.91{\%} and a weighted F1 score of 51.31{\%}. We also present improved results in this paper, which we obtained after the competition ended. Simply by adding more regularization into our model to make it more suitable for test data that comes from a different distribution than training data, we obtain an accuracy of 51.82{\%} and a weighted F1 score of 52.18{\%}. Furthermore, the proposed approach has an important advantage in that it is language independent and linguistic theory neutral, as it does not require any NLP tools.
HeLI, a Word-Based Backoff Method for Language Identification
Authors: Tommi Jauhiainen, Krister Lind{\'e}n, Heidi Jauhiainen
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Language Identification, Position

In this paper we describe the Helsinki language identification method, HeLI, and the resources we created for and used in the 3rd edition of the Discriminating between Similar Languages (DSL) shared task, which was organized as part of the VarDial 2016 workshop. The shared task comprised of a total of 8 tracks, of which we participated in 7. The shared task had a record number of participants, with 17 teams providing results for the closed track of the test set A. Our system reached the 2nd position in 4 tracks (A closed and open, B1 open and B2 open) and in this paper we are focusing on the methods and data used for those tracks. We describe our word-based backoff method in mathematical notation. We also describe how we selected the corpus we used in the open tracks.
ASIREM Participation at the Discriminating Similar Languages Shared Task 2016
Authors: Wafia Adouane, Nasredine Semmar, Richard Johansson
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Dialect Identification, Language Identification, Task 2

This paper presents the system built by ASIREM team for the Discriminating between Similar Languages (DSL) Shared task 2016. It describes the system which uses character-based and word-based n-grams separately. ASIREM participated in both sub-tasks (sub-task 1 and sub-task 2) and in both open and closed tracks. For the sub-task 1 which deals with Discriminating between similar languages and national language varieties, the system achieved an accuracy of 87.79{\%} on the closed track, ending up ninth (the best results being 89.38{\%}). In sub-task 2, which deals with Arabic dialect identification, the system achieved its best performance using character-based n-grams (49.67{\%} accuracy), ranking fourth in the closed track (the best result being 51.16{\%}), and an accuracy of 53.18{\%}, ranking first in the open track.
Comparing Two Basic Methods for Discriminating Between Similar Languages and Varieties
Authors: Pablo Gamallo, I{\~n}aki Alegria, Jos{\'e} Ramom Pichel, Manex Agirrezabal
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Automatic Speech Recognition (ASR), General Classification, Language Identification, Speech Recognition, Vocal Bursts Valence Prediction

This article describes the systems submitted by the Citius{\_}Ixa{\_}Imaxin team to the Discriminating Similar Languages Shared Task 2016. The systems are based on two different strategies: classification with ranked dictionaries and Naive Bayes classifiers. The results of the evaluation show that ranking dictionaries are more sound and stable across different domains while basic bayesian models perform reasonably well on in-domain datasets, but their performance drops when they are applied on out-of-domain texts.
Advances in Ngram-based Discrimination of Similar Languages
Authors: Cyril Goutte, Serge L{\'e}ger
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12

We describe the systems entered by the National Research Council in the 2016 shared task on discriminating similar languages. Like previous years, we relied on character ngram features, and a mixture of discriminative and generative statistical classifiers. We mostly investigated the influence of the amount of data on the performance, in the open task, and compared the two-stage approach (predicting language/group, then variant) to a flat approach. Results suggest that ngrams are still state-of-the-art for language and variant identification, and that additional data has a small but decisive impact.
Discrimination between Similar Languages, Varieties and Dialects using CNN- and LSTM-based Deep Neural Networks
Authors: Chinnappa Guggilla
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Dialect Identification, Information Retrieval, Language Identification, Machine Translation, Speaker Identification, Text Generation

In this paper, we describe a system (CGLI) for discriminating similar languages, varieties and dialects using convolutional neural networks (CNNs) and long short-term memory (LSTM) neural networks. We have participated in the Arabic dialect identification sub-task of DSL 2016 shared task for distinguishing different Arabic language texts under closed submission track. Our proposed approach is language independent and works for discriminating any given set of languages, varieties, and dialects. We have obtained 43.29{\%} weighted-F1 accuracy in this sub-task using CNN approach using default network parameters.
Language and Dialect Discrimination Using Compression-Inspired Language Models
Authors: Paul McNamee
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Authorship Attribution, Dialect Identification, General Classification, Language Identification, Language Modeling, Language Modelling, Spam detection, text-classification, Text Classification, Text Compression

The DSL 2016 shared task continued previous evaluations from 2014 and 2015 that facilitated the study of automated language and dialect identification. This paper describes results for this year{'}s shared task and from several related experiments conducted at the Johns Hopkins University Human Language Technology Center of Excellence (JHU HLTCOE). Previously the HLTCOE has explored the use of compression-inspired language modeling for language and dialect identification, using news, Wikipedia, blog post, and Twitter corpora. The technique we have relied upon is based on prediction by partial matching (PPM), a state of the art text compression technique. Due to the close relationship between adaptive compression and language modeling, such compression techniques can also be applied to multi-way text classification problems, and previous studies have examined tasks such as authorship attribution, email spam detection, and topical classification. We applied our approach to the multi-class decision that considered each dialect or language as a possibility for the given shared task input line. Results for test-set A were in accord with our expectations, however results for test-sets B and C appear to be markedly worse. We had not anticipated the inclusion of multiple communications in differing languages in test-set B (social media) input lines, and had not expected the test-set C (dialectal Arabic) data to be represented phonetically instead of in native orthography.
Arabic Language WEKA-Based Dialect Classifier for Arabic Automatic Speech Recognition Transcripts
Authors: Areej Alshutayri, Eric Atwell, Abdulrahman Alosaimy, James Dickins, Michael Ingleby, Janet Watson
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Dialect Identification, Language Identification, speech-recognition, Speech Recognition

This paper describes an Arabic dialect identification system which we developed for the Discriminating Similar Languages (DSL) 2016 shared task. We classified Arabic dialects by using Waikato Environment for Knowledge Analysis (WEKA) data analytic tool which contains many alternative filters and classifiers for machine learning. We experimented with several classifiers and the best accuracy was achieved using the Sequential Minimal Optimization (SMO) algorithm for training and testing process set to three different feature-sets for each testing process. Our approach achieved an accuracy equal to 42.85{\%} which is considerably worse in comparison to the evaluation scores on the training set of 80-90{\%} and with training set {``}60:40{''} percentage split which achieved accuracy around 50{\%}. We observed that Buckwalter transcripts from the Saarland Automatic Speech Recognition (ASR) system are given without short vowels, though the Buckwalter system has notation for these. We elaborate such observations, describe our methods and analyse the training dataset.
An Unsupervised Morphological Criterion for Discriminating Similar Languages
Authors: Adrien Barbaresi
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Language Identification, Text Categorization

In this study conducted on the occasion of the Discriminating between Similar Languages shared task, I introduce an additional decision factor focusing on the token and subtoken level. The motivation behind this submission is to test whether a morphologically-informed criterion can add linguistically relevant information to global categorization and thus improve performance. The contributions of this paper are (1) a description of the unsupervised, low-resource method; (2) an evaluation and analysis of its raw performance; and (3) an assessment of its impact within a model comprising common indicators used in language identification. I present and discuss the systems used in the task A, a 12-way language identification task comprising varieties of five main language groups. Additionally I introduce a new off-the-shelf Naive Bayes classifier using a contrastive word and subword n-gram model ({``}Bayesline{''}) which outperforms the best submissions.