Taraka Rama


2020

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Probing Multilingual BERT for Genetic and Typological Signals
Taraka Rama | Lisa Beinborn | Steffen Eger
Proceedings of the 28th International Conference on Computational Linguistics

We probe the layers in multilingual BERT (mBERT) for phylogenetic and geographic language signals across 100 languages and compute language distances based on the mBERT representations. We 1) employ the language distances to infer and evaluate language trees, finding that they are close to the reference family tree in terms of quartet tree distance, 2) perform distance matrix regression analysis, finding that the language distances can be best explained by phylogenetic and worst by structural factors and 3) present a novel measure for measuring diachronic meaning stability (based on cross-lingual representation variability) which correlates significantly with published ranked lists based on linguistic approaches. Our results contribute to the nascent field of typological interpretability of cross-lingual text representations.

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Disentangling dialects: a neural approach to Indo-Aryan historical phonology and subgrouping
Chundra Cathcart | Taraka Rama
Proceedings of the 24th Conference on Computational Natural Language Learning

This paper seeks to uncover patterns of sound change across Indo-Aryan languages using an LSTM encoder-decoder architecture. We augment our models with embeddings represent-ing language ID, part of speech, and other features such as word embeddings. We find that a highly augmented model shows highest accuracy in predicting held-out forms, and investigate other properties of interest learned by our models’ representations. We outline extensions to this architecture that can better capture variation in Indo-Aryan sound change.

2019

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An Automated Framework for Fast Cognate Detection and Bayesian Phylogenetic Inference in Computational Historical Linguistics
Taraka Rama | Johann-Mattis List
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

We present a fully automated workflow for phylogenetic reconstruction on large datasets, consisting of two novel methods, one for fast detection of cognates and one for fast Bayesian phylogenetic inference. Our results show that the methods take less than a few minutes to process language families that have so far required large amounts of time and computational power. Moreover, the cognates and the trees inferred from the method are quite close, both to gold standard cognate judgments and to expert language family trees. Given its speed and ease of application, our framework is specifically useful for the exploration of very large datasets in historical linguistics.

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Sigmorphon 2019 Task 2 system description paper: Morphological analysis in context for many languages, with supervision from only a few
Brad Aiken | Jared Kelly | Alexis Palmer | Suleyman Olcay Polat | Taraka Rama | Rodney Nielsen
Proceedings of the 16th Workshop on Computational Research in Phonetics, Phonology, and Morphology

This paper presents the UNT HiLT+Ling system for the Sigmorphon 2019 shared Task 2: Morphological Analysis and Lemmatization in Context. Our core approach focuses on the morphological tagging task; part-of-speech tagging and lemmatization are treated as secondary tasks. Given the highly multilingual nature of the task, we propose an approach which makes minimal use of the supplied training data, in order to be extensible to languages without labeled training data for the morphological inflection task. Specifically, we use a parallel Bible corpus to align contextual embeddings at the verse level. The aligned verses are used to build cross-language translation matrices, which in turn are used to map between embedding spaces for the various languages. Finally, we use sets of inflected forms, primarily from a high-resource language, to induce vector representations for individual UniMorph tags. Morphological analysis is performed by matching vector representations to embeddings for individual tokens. While our system results are dramatically below the average system submitted for the shared task evaluation campaign, our method is (we suspect) unique in its minimal reliance on labeled training data.

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Regression or classification? Automated Essay Scoring for Norwegian
Stig Johan Berggren | Taraka Rama | Lilja Øvrelid
Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications

In this paper we present first results for the task of Automated Essay Scoring for Norwegian learner language. We analyze a number of properties of this task experimentally and assess (i) the formulation of the task as either regression or classification, (ii) the use of various non-neural and neural machine learning architectures with various types of input representations, and (iii) applying multi-task learning for joint prediction of essay scoring and native language identification. We find that a GRU-based attention model trained in a single-task setting performs best at the AES task.

2018

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Towards identifying the optimal datasize for lexically-based Bayesian inference of linguistic phylogenies
Taraka Rama | Søren Wichmann
Proceedings of the 27th International Conference on Computational Linguistics

Bayesian linguistic phylogenies are standardly based on cognate matrices for words referring to a fix set of meanings—typically around 100-200. To this day there has not been any empirical investigation into which datasize is optimal. Here we determine, across a set of language families, the optimal number of meanings required for the best performance in Bayesian phylogenetic inference. We rank meanings by stability, infer phylogenetic trees using first the most stable meaning, then the two most stable meanings, and so on, computing the quartet distance of the resulting tree to the tree proposed by language family experts at each step of datasize increase. When a gold standard tree is not available we propose to instead compute the quartet distance between the tree based on the n-most stable meaning and the one based on the n + 1-most stable meanings, increasing n from 1 to N − 1, where N is the total number of meanings. The assumption here is that the value of n for which the quartet distance begins to stabilize is also the value at which the quality of the tree ceases to improve. We show that this assumption is borne out. The results of the two methods vary across families, and the optimal number of meanings appears to correlate with the number of languages under consideration.

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Are Automatic Methods for Cognate Detection Good Enough for Phylogenetic Reconstruction in Historical Linguistics?
Taraka Rama | Johann-Mattis List | Johannes Wahle | Gerhard Jäger
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)

We evaluate the performance of state-of-the-art algorithms for automatic cognate detection by comparing how useful automatically inferred cognates are for the task of phylogenetic inference compared to classical manually annotated cognate sets. Our findings suggest that phylogenies inferred from automated cognate sets come close to phylogenies inferred from expert-annotated ones, although on average, the latter are still superior. We conclude that future work on phylogenetic reconstruction can profit much from automatic cognate detection. Especially where scholars are merely interested in exploring the bigger picture of a language family’s phylogeny, algorithms for automatic cognate detection are a useful complement for current research on language phylogenies.

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Tübingen-Oslo at SemEval-2018 Task 2: SVMs perform better than RNNs in Emoji Prediction
Çağrı Çöltekin | Taraka Rama
Proceedings of the 12th International Workshop on Semantic Evaluation

This paper describes our participation in the SemEval-2018 task Multilingual Emoji Prediction. We participated in both English and Spanish subtasks, experimenting with support vector machines (SVMs) and recurrent neural networks. Our SVM classifier obtained the top rank in both subtasks with macro-averaged F1-measures of 35.99% for English and 22.36% for Spanish data sets. Similar to a few earlier attempts, the results with neural networks were not on par with linear SVMs.

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Experiments with Universal CEFR Classification
Sowmya Vajjala | Taraka Rama
Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications

The Common European Framework of Reference (CEFR) guidelines describe language proficiency of learners on a scale of 6 levels. While the description of CEFR guidelines is generic across languages, the development of automated proficiency classification systems for different languages follow different approaches. In this paper, we explore universal CEFR classification using domain-specific and domain-agnostic, theory-guided as well as data-driven features. We report the results of our preliminary experiments in monolingual, cross-lingual, and multilingual classification with three languages: German, Czech, and Italian. Our results show that both monolingual and multilingual models achieve similar performance, and cross-lingual classification yields lower, but comparable results to monolingual classification.

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Tübingen-Oslo Team at the VarDial 2018 Evaluation Campaign: An Analysis of N-gram Features in Language Variety Identification
Çağrı Çöltekin | Taraka Rama | Verena Blaschke
Proceedings of the Fifth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial 2018)

This paper describes our systems for the VarDial 2018 evaluation campaign. We participated in all language identification tasks, namely, Arabic dialect identification (ADI), German dialect identification (GDI), discriminating between Dutch and Flemish in Subtitles (DFS), and Indo-Aryan Language Identification (ILI). In all of the tasks, we only used textual transcripts (not using audio features for ADI). We submitted system runs based on support vector machine classifiers (SVMs) with bag of character and word n-grams as features, and gated bidirectional recurrent neural networks (RNNs) using units of characters and words. Our SVM models outperformed our RNN models in all tasks, obtaining the first place on the DFS task, third place on the ADI task, and second place on others according to the official rankings. As well as describing the models we used in the shared task participation, we present an analysis of the n-gram features used by the SVM models in each task, and also report additional results (that were run after the official competition deadline) on the GDI surprise dialect track.

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Iterative development of family history annotation guidelines using a synthetic corpus of clinical text
Taraka Rama | Pål Brekke | Øystein Nytrø | Lilja Øvrelid
Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis

In this article, we describe the development of annotation guidelines for family history information in Norwegian clinical text. We make use of incrementally developed synthetic clinical text describing patients’ family history relating to cases of cardiac disease and present a general methodology which integrates the synthetically produced clinical statements and guideline development. We analyze inter-annotator agreement based on the developed guidelines and present results from experiments aimed at evaluating the validity and applicability of the annotated corpus using machine learning techniques. The resulting annotated corpus contains 477 sentences and 6030 tokens. Both the annotation guidelines and the annotated corpus are made freely available and as such constitutes the first publicly available resource of Norwegian clinical text.

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Drug-Use Identification from Tweets with Word and Character N-Grams
Çağrı Çöltekin | Taraka Rama
Proceedings of the 2018 EMNLP Workshop SMM4H: The 3rd Social Media Mining for Health Applications Workshop & Shared Task

This paper describes our systems in social media mining for health applications (SMM4H) shared task. We participated in all four tracks of the shared task using linear models with a combination of character and word n-gram features. We did not use any external data or domain specific information. The resulting systems achieved above-average scores among other participating systems, with F1-scores of 91.22, 46.8, 42.4, and 85.53 on tasks 1, 2, 3, and 4 respectively.

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Using Universal Dependencies in cross-linguistic complexity research
Aleksandrs Berdicevskis | Çağrı Çöltekin | Katharina Ehret | Kilu von Prince | Daniel Ross | Bill Thompson | Chunxiao Yan | Vera Demberg | Gary Lupyan | Taraka Rama | Christian Bentz
Proceedings of the Second Workshop on Universal Dependencies (UDW 2018)

We evaluate corpus-based measures of linguistic complexity obtained using Universal Dependencies (UD) treebanks. We propose a method of estimating robustness of the complexity values obtained using a given measure and a given treebank. The results indicate that measures of syntactic complexity might be on average less robust than those of morphological complexity. We also estimate the validity of complexity measures by comparing the results for very similar languages and checking for unexpected differences. We show that some of those differences that arise can be diminished by using parallel treebanks and, more importantly from the practical point of view, by harmonizing the language-specific solutions in the UD annotation.

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Similarity Dependent Chinese Restaurant Process for Cognate Identification in Multilingual Wordlists
Taraka Rama
Proceedings of the 22nd Conference on Computational Natural Language Learning

We present and evaluate two similarity dependent Chinese Restaurant Process (sd-CRP) algorithms at the task of automated cognate detection. The sd-CRP clustering algorithms do not require any predefined threshold for detecting cognate sets in a multilingual word list. We evaluate the performance of the algorithms on six language families (more than 750 languages) and find that both the sd-CRP variants performs as well as InfoMap and better than UPGMA at the task of inferring cognate clusters. The algorithms presented in this paper are family agnostic and can be applied to any linguistically under-studied language family.

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Tübingen-Oslo system at SIGMORPHON shared task on morphological inflection. A multi-tasking multilingual sequence to sequence model.
Taraka Rama | Çağrı Çöltekin
Proceedings of the CoNLL–SIGMORPHON 2018 Shared Task: Universal Morphological Reinflection

2017

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Computational analysis of Gondi dialects
Taraka Rama | Çağrı Çöltekin | Pavel Sofroniev
Proceedings of the Fourth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial)

This paper presents a computational analysis of Gondi dialects spoken in central India. We present a digitized data set of the dialect area, and analyze the data using different techniques from dialectometry, deep learning, and computational biology. We show that the methods largely agree with each other and with the earlier non-computational analyses of the language group.

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Tübingen system in VarDial 2017 shared task: experiments with language identification and cross-lingual parsing
Çağrı Çöltekin | Taraka Rama
Proceedings of the Fourth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial)

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.

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Fewer features perform well at Native Language Identification task
Taraka Rama | Çağrı Çöltekin
Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications

This paper describes our results at the NLI shared task 2017. We participated in essays, speech, and fusion task that uses text, speech, and i-vectors for the task of identifying the native language of the given input. In the essay track, a linear SVM system using word bigrams and character 7-grams performed the best. In the speech track, an LDA classifier based only on i-vectors performed better than a combination system using text features from speech transcriptions and i-vectors. In the fusion task, we experimented with systems that used combination of i-vectors with higher order n-grams features, combination of i-vectors with word unigrams, a mean probability ensemble, and a stacked ensemble system. Our finding is that word unigrams in combination with i-vectors achieve higher score than systems trained with larger number of n-gram features. Our best-performing systems achieved F1-scores of 87.16%, 83.33% and 91.75% on the essay track, the speech track and the fusion track respectively.

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A Telugu treebank based on a grammar book
Taraka Rama | Sowmya Vajjala
Proceedings of the 16th International Workshop on Treebanks and Linguistic Theories

2016

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Siamese Convolutional Networks for Cognate Identification
Taraka Rama
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

In this paper, we present phoneme level Siamese convolutional networks for the task of pair-wise cognate identification. We represent a word as a two-dimensional matrix and employ a siamese convolutional network for learning deep representations. We present siamese architectures that jointly learn phoneme level feature representations and language relatedness from raw words for cognate identification. Compared to previous works, we train and test on larger and realistic datasets; and, show that siamese architectures consistently perform better than traditional linear classifier approach.

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Discriminating Similar Languages with Linear SVMs and Neural Networks
Çağrı Çöltekin | Taraka Rama
Proceedings of the Third Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial3)

This paper describes the systems we experimented with for participating in the discriminating between similar languages (DSL) shared task 2016. We submitted results of a single system based on support vector machines (SVM) with linear kernel and using character ngram features, which obtained the first rank at the closed training track for test set A. Besides the linear SVM, we also report additional experiments with a number of deep learning architectures. Despite our intuition that non-linear deep learning methods should be advantageous, linear models seems to fare better in this task, at least with the amount of data and the amount of effort we spent on tuning these models.

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LSTM Autoencoders for Dialect Analysis
Taraka Rama | Çağrı Çöltekin
Proceedings of the Third Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial3)

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.

2015

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Automatic cognate identification with gap-weighted string subsequences.
Taraka Rama
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2014

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Linguistic landscaping of South Asia using digital language resources: Genetic vs. areal linguistics
Lars Borin | Anju Saxena | Taraka Rama | Bernard Comrie
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

Like many other research fields, linguistics is entering the age of big data. We are now at a point where it is possible to see how new research questions can be formulated - and old research questions addressed from a new angle or established results verified - on the basis of exhaustive collections of data, rather than small, carefully selected samples. For example, South Asia is often mentioned in the literature as a classic example of a linguistic area, but there is no systematic, empirical study substantiating this claim. Examination of genealogical and areal relationships among South Asian languages requires a large-scale quantitative and qualitative comparative study, encompassing more than one language family. Further, such a study cannot be conducted manually, but needs to draw on extensive digitized language resources and state-of-the-art computational tools. We present some preliminary results of our large-scale investigation of the genealogical and areal relationships among the languages of this region, based on the linguistic descriptions available in the 19 tomes of Grierson’s monumental “Linguistic Survey of India” (1903-1927), which is currently being digitized with the aim of turning the linguistic information in the LSI into a digital language resource suitable for a broad array of linguistic investigations.

2012

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How Good are Typological Distances for Determining Genealogical Relationships among Languages?
Taraka Rama | Prasanth Kolachina
Proceedings of COLING 2012: Posters

2011

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Estimating Language Relationships from a Parallel Corpus. A Study of the Europarl Corpus
Taraka Rama | Lars Borin
Proceedings of the 18th Nordic Conference of Computational Linguistics (NODALIDA 2011)

2009

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Modeling Machine Transliteration as a Phrase Based Statistical Machine Translation Problem
Taraka Rama | Karthik Gali
Proceedings of the 2009 Named Entities Workshop: Shared Task on Transliteration (NEWS 2009)

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From Bag of Languages to Family Trees From Noisy Corpus
Taraka Rama | Anil Kumar Singh
Proceedings of the International Conference RANLP-2009

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Modeling Letter-to-Phoneme Conversion as a Phrase Based Statistical Machine Translation Problem with Minimum Error Rate Training
Taraka Rama | Anil Kumar Singh | Sudheer Kolachina
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Student Research Workshop and Doctoral Consortium