ymoslem/acl-abstracts huggingface dataset:
Machine Translation from Standard German to Alemannic Dialects (2022)

Machine translation has been researched using deep neural networks in recent years. These networks require lots of data to learn abstract representations of the input stored in continuous vectors. Dialect translation has become more important since the advent of social media. In particular, when dialect speakers and standard language speakers no longer understand each other, machine translation is of rising concern. Usually, dialect translation is a typical low-resourced language setting facing data scarcity problems. Additionally, spelling inconsistencies due to varying pronunciations and the lack of spelling rules complicate translation. This paper presents the best-performing approaches to handle these problems for Alemannic dialects. The results show that back-translation and conditioning on dialectal manifestations achieve the most remarkable enhancement over the baseline. Using back-translation, a significant gain of +4.5 over the strong transformer baseline of 37.3 BLEU points is accomplished. Differentiating between several Alemannic dialects instead of treating Alemannic as one dialect leads to substantial improvements: Multi-dialectal translation surpasses the baseline on the dialectal test sets. However, training individual models outperforms the multi-dialectal approach. There, improvements range from 7.5 to 10.6 BLEU points over the baseline depending on the dialect.
Question Answering Classification for Amharic Social Media Community Based Questions (2022)

In this work, we build a Question Answering (QA) classification dataset from a social media platform, namely the Telegram public channel called @AskAnythingEthiopia. The channel has more than 78k subscribers and has existed since May 31, 2019. The platform allows asking questions that belong to various domains, like politics, economics, health, education, and so on. Since the questions are posed in a mixed-code, we apply different strategies to pre-process the dataset. Questions are posted in Amharic, English, or Amharic but in a Latin script. As part of the pre-processing tools, we build a Latin to Ethiopic Script transliteration tool. We collect 8k Amharic and 24K transliterated questions and develop deep learning-based questions answering classifiers that attain as high as an F-score of 57.29 in 20 different question classes or categories. The datasets and pre-processing scripts are open-sourced to facilitate further research on the Amharic community-based question answering.
Automatic Detection of Morphological Processes in the Yor\`ub\'a Language (2022)

Automatic morphology induction is important for computational processing of natural language. In resource-scarce languages in particular, it offers the possibility of supplementing data-driven strategies of Natural Language Processing with morphological rules that may cater for out-of-vocabulary words. Unfortunately, popular approaches to unsupervised morphology induction do not work for some of the most productive morphological processes of the Yor{\`u}b{\'a} language. To the best of our knowledge, the automatic induction of such morphological processes as full and partial reduplication, infixation, interfixation, compounding and other morphological processes, particularly those based on the affixation of stem-derived morphemes have not been adequately addressed in the literature. This study proposes a method for the automatic detection of stem-derived morphemes in Yor{\`u}b{\'a}. Words in a Yor{\`u}b{\'a} lexicon of 14,670 word-tokens were clustered around {\textquotedblleft}word-labels{\textquotedblright}. A word-label is a textual proxy of the patterns imposed on words by the morphological processes through which they were formed. Results confirm a conjectured significant difference between the predicted and observed probabilities of word-labels motivated by stem-derived morphemes. This difference was used as basis for automatic identification of words formed by the affixation of stem-derived morphemes. Keywords: Unsupervised Morphology Induction, Recurrent Partials, Recurrent Patterns, Stem-derived Morphemes, Word-labels.
Evaluating Unsupervised Approaches to Morphological Segmentation for Wolastoqey (2022)

Finite-state approaches to morphological analysis have been shown to improve the performance of natural language processing systems for polysynthetic languages, in-which words are generally composed of many morphemes, for tasks such as language modelling (Schwartz et al., 2020). However, finite-state morphological analyzers are expensive to construct and require expert knowledge of a language`s structure. Currently, there is no broad-coverage finite-state model of morphology for Wolastoqey, also known as Passamaquoddy-Maliseet, an endangered low-resource Algonquian language. As this is the case, in this paper, we investigate using two unsupervised models, MorphAGram and Morfessor, to obtain morphological segmentations for Wolastoqey. We train MorphAGram and Morfessor models on a small corpus of Wolastoqey words and evaluate using two an notated datasets. Our results indicate that MorphAGram outperforms Morfessor for morphological segmentation of Wolastoqey.
Baseline English and Maltese-English Classification Models for Subjectivity Detection, Sentiment Analysis, Emotion Analysis, Sarcasm Detection, and Irony Detection (2022)

This paper presents baseline classification models for subjectivity detection, sentiment analysis, emotion analysis, sarcasm detection, and irony detection. All models are trained on user-generated content gathered from newswires and social networking services, in three different languages: English {---}a high-resourced language, Maltese {---}a low-resourced language, and Maltese-English {---}a code-switched language. Traditional supervised algorithms namely, Support Vector Machines, Na{\"i}ve Bayes, Logistic Regression, Decision Trees, and Random Forest, are used to build a baseline for each classification task, namely subjectivity, sentiment polarity, emotion, sarcasm, and irony. Baseline models are established at a monolingual (English) level and at a code-switched level (Maltese-English). Results obtained from all the classification models are presented.
Building Open-source Speech Technology for Low-resource Minority Languages with S\'aMi as an Example -- Tools, Methods and Experiments (2022)

This paper presents a work-in-progress report of an open-source speech technology project for indigenous Sami languages. A less detailed description of this work has been presented in a more general paper about the whole GiellaLT language infrastructure, submitted to the LREC 2022 main conference. At this stage, we have designed and collected a text corpus specifically for developing speech technology applications, namely Text-to-speech (TTS) and Automatic speech recognition (ASR) for the Lule and North Sami languages. We have also piloted and experimented with different speech synthesis technologies using a miniature speech corpus as well as developed tools for effective processing of large spoken corpora. Additionally, we discuss effective and mindful use of the speech corpus and also possibilities to use found/archive materials for training an ASR model for these languages.
Investigating the Quality of Static Anchor Embeddings from Transformers for Under-Resourced Languages (2022)

This paper reports on experiments for cross-lingual transfer using the anchor-based approach of Schuster et al. (2019) for English and a low-resourced language, namely Hindi. For the sake of comparison, we also evaluate the approach on three very different higher-resourced languages, viz. Dutch, Russian and Chinese. Initially designed for ELMo embeddings, we analyze the approach for the more recent BERT family of transformers for a variety of tasks, both mono and cross-lingual. The results largely prove that like most other cross-lingual transfer approaches, the static anchor approach is underwhelming for the low-resource language, while performing adequately for the higher resourced ones. We attempt to provide insights into both the quality of the anchors, and the performance for low-shot cross-lingual transfer to better understand this performance gap. We make the extracted anchors and the modified train and test sets available for future research at \url{https://github.com/pranaydeeps/Vyaapak}
Introducing YakuToolkit. Yakut Treebank and Morphological Analyzer. (2022)

This poster presents the first publicly available treebank of Yakut, a Turkic language spoken in Russia, and a morphological analyzer for this language. The treebank was annotated following the Universal Dependencies (UD) framework and the mor- phological analyzer can directly access and use its data. Yakut is an under-represented language whose prominence can be raised by making reliably annotated data and NLP tools that could process it freely accessible. The publication of both the treebank and the analyzer serves this purpose with the prospect of evolving into a benchmark for the development of NLP online tools for other languages of the Turkic family in the future.
A Language Model for Spell Checking of Educational Texts in Kurdish (Sorani) (2022)

Spell checkers are an integrated feature of most software applications handling text inputs. When we write an email or compile a report on a desktop or a smartphone editor, a spell checker could be activated that assists us to write more correctly. However, this assistance does not exist for all languages equally. The Kurdish language, which still is considered a less-resourced language, currently lacks spell checkers for its various dialects. We present a trigram language model for the Sorani dialect of the Kurdish language that is created using educational text. We also showcase a spell checker for the Sorani dialect of Kurdish that can assist in writing texts in the Persian/Arabic script. The spell checker was developed as a testing environment for the language model. Primarily, we use the probabilistic method and our trigram language model with Stupid Backoff smoothing for the spell checking algorithm. Our spell checker has been trained on the KTC (Kurdish Textbook Corpus) dataset. Hence the system aims at assisting spell checking in the related context. We test our approach by developing a text processing environment that checks for spelling errors on a word and context basis. It suggests a list of corrections for misspelled words. The developed spell checker shows 88.54{\%} accuracy on the texts in the related context and it has an F1 score of 43.33{\%}, and the correct suggestion has an 85{\%} chance of being in the top three positions of the corrections.
SimRelUz: Similarity and Relatedness Scores as a Semantic Evaluation Dataset for Uzbek Language (2022)

Semantic relatedness between words is one of the core concepts in natural language processing, thus making semantic evaluation an important task. In this paper, we present a semantic model evaluation dataset: SimRelUz - a collection of similarity and relatedness scores of word pairs for the low-resource Uzbek language. The dataset consists of more than a thousand pairs of words carefully selected based on their morphological features, occurrence frequency, semantic relation, as well as annotated by eleven native Uzbek speakers from different age groups and gender. We also paid attention to the problem of dealing with rare words and out-of-vocabulary words to thoroughly evaluate the robustness of semantic models.
ENRICH4ALL: A First Luxembourgish BERT Model for a Multilingual Chatbot (2022)

Machine Translation (MT)-empowered chatbots are not established yet, however, we see an amazing future breaking language barriers and enabling conversation in multiple languages without time-consuming language model building and training, particularly for under-resourced languages. In this paper we focus on the under-resourced Luxembourgish language. This article describes the experiments we have done with a dataset containing administrative questions that we have manually created to offer BERT QA capabilities to a multilingual chatbot. The chatbot supports visual dialog flow diagram creation (through an interface called BotStudio) in which a dialog node manages the user question at a specific step. Dialog nodes can be matched to the user`s question by using a BERT classification model which labels the question with a dialog node label.
Multilingualism Encourages Recursion: a Transfer Study with mBERT (2022)

The present work constitutes an attempt to investigate the relational structures learnt by mBERT, a multilingual transformer-based network, with respect to different cross-linguistic regularities proposed in the fields of theoretical and quantitative linguistics. We pursued this objective by relying on a zero-shot transfer experiment, evaluating the model`s ability to generalize its native task to artificial languages that could either respect or violate some proposed language universal, and comparing its performance to the output of BERT, a monolingual model with an identical configuration. We created four artificial corpora through a Probabilistic Context-Free Grammar by manipulating the distribution of tokens and the structure of their dependency relations. We showed that while both models were favoured by a Zipfian distribution of the tokens and by the presence of head-dependency type structures, the multilingual transformer network exhibited a stronger reliance on hierarchical cues compared to its monolingual counterpart.
Word-order Typology in Multilingual BERT: A Case Study in Subordinate-Clause Detection (2022)

The capabilities and limitations of BERT and similar models are still unclear when it comes to learning syntactic abstractions, in particular across languages. In this paper, we use the task of subordinate-clause detection within and across languages to probe these properties. We show that this task is deceptively simple, with easy gains offset by a long tail of harder cases, and that BERT`s zero-shot performance is dominated by word-order effects, mirroring the SVO/VSO/SOV typology.
Typological Word Order Correlations with Logistic Brownian Motion (2022)

In this study we address the question to what extent syntactic word-order traits of different languages have evolved under correlation and whether such dependencies can be found universally across all languages or restricted to specific language families. To do so, we use logistic Brownian Motion under a Bayesian framework to model the trait evolution for 768 languages from 34 language families. We test for trait correlations both in single families and universally over all families. Separate models reveal no universal correlation patterns and Bayes Factor analysis of models over all covered families also strongly indicate lineage specific correlation patters instead of universal dependencies.
Cross-linguistic Comparison of Linguistic Feature Encoding in BERT Models for Typologically Different Languages (2022)

Though recently there have been an increased interest in how pre-trained language models encode different linguistic features, there is still a lack of systematic comparison between languages with different morphology and syntax. In this paper, using BERT as an example of a pre-trained model, we compare how three typologically different languages (English, Korean, and Russian) encode morphology and syntax features across different layers. In particular, we contrast languages which differ in a particular aspect, such as flexibility of word order, head directionality, morphological type, presence of grammatical gender, and morphological richness, across four different tasks.
Tweaking UD Annotations to Investigate the Placement of Determiners, Quantifiers and Numerals in the Noun Phrase (2022)

We describe a methodology to extract with finer accuracy word order patterns from texts automatically annotated with Universal Dependency (UD) trained parsers. We use the methodology to quantify the word order entropy of determiners, quantifiers and numerals in ten Indo-European languages, using UD-parsed texts from a parallel corpus of prosaic texts. Our results suggest that the combinations of different UD annotation layers, such as UD Relations, Universal Parts of Speech and lemma, and the introduction of language-specific lists of closed-category lemmata has the two-fold effect of improving the quality of analysis and unveiling hidden areas of variability in word order patterns.
A Database for Modal Semantic Typology (2022)

This paper introduces a database for crosslinguistic modal semantics. The purpose of this database is to (1) enable ongoing consolidation of modal semantic typological knowledge into a repository according to uniform data standards and to (2) provide data for investigations in crosslinguistic modal semantic theory and experiments explaining such theories. We describe the kind of semantic variation that the database aims to record, the format of the data, and a current snapshot of the database, emphasizing access and contribution to the database in light of the goals above. We release the database at \url{https://clmbr.shane.st/modal-typology}.
The SIGTYP 2022 Shared Task on the Prediction of Cognate Reflexes (2022)

This study describes the structure and the results of the SIGTYP 2022 shared task on the prediction of cognate reflexes from multilingual wordlists. We asked participants to submit systems that would predict words in individual languages with the help of cognate words from related languages. Training and surprise data were based on standardized multilingual wordlists from several language families. Four teams submitted a total of eight systems, including both neural and non-neural systems, as well as systems adjusted to the task and systems using more general settings. While all systems showed a rather promising performance, reflecting the overwhelming regularity of sound change, the best performance throughout was achieved by a system based on convolutional networks originally designed for image restoration.
Bayesian Phylogenetic Cognate Prediction (2022)

In J{\"a}ger (2019) a computational framework was defined to start from parallel word lists of related languages and infer the corresponding vocabulary of the shared proto-language. The SIGTYP 2022 Shared Task is closely related. The main difference is that what is to be reconstructed is not the proto-form but an unknown word from an extant language. The system described here is a re-implementation of the tools used in the mentioned paper, adapted to the current task.
Mockingbird at the SIGTYP 2022 Shared Task: Two Types of Models for the Prediction of Cognate Reflexes (2022)

The SIGTYP 2022 shared task concerns the problem of word reflex generation in a target language, given cognate words from a subset of related languages. We present two systems to tackle this problem, covering two very different modeling approaches. The first model extends transformer-based encoder-decoder sequence-to-sequence modeling, by encoding all available input cognates in parallel, and having the decoder attend to the resulting joint representation during inference. The second approach takes inspiration from the field of image restoration, where models are tasked with recovering pixels in an image that have been masked out. For reflex generation, the missing reflexes are treated as {\textquotedblleft}masked pixels{\textquotedblright} in an {\textquotedblleft}image{\textquotedblright} which is a representation of an entire cognate set across a language family. As in the image restoration case, cognate restoration is performed with a convolutional network.
A Transformer Architecture for the Prediction of Cognate Reflexes (2022)

This paper presents the transformer model built to participate in the SIGTYP 2022 Shared Task on the Prediction of Cognate Reflexes. It consists of an encoder-decoder architecture with multi-head attention mechanism. Its output is concatenated with the one hot encoding of the language label of an input character sequence to predict a target character sequence. The results show that the transformer outperforms the baseline rule-based system only partially.
Approaching Reflex Predictions as a Classification Problem Using Extended Phonological Alignments (2022)

This work describes an implementation of the {\textquotedblleft}extended alignment{\textquotedblright} model for cognate reflex prediction submitted to the {\textquotedblleft}SIGTYP 2022 Shared Task on the Prediction of Cognate Reflexes{\textquotedblright}. Similarly to List et al. (2022a), the technique involves an automatic extension of sequence alignments with multilayered vectors that encode informational tiers on both site-specific traits, such as sound classes and distinctive features, as well as contextual and suprasegmental ones, conveyed by cross-site referrals and replication. The method allows to generalize the problem of cognate reflex prediction as a classification problem, with models trained using a parallel corpus of cognate sets. A model using random forests is trained and evaluated on the shared task for reflex prediction, and the experimental results are presented and discussed along with some differences to other implementations.
Investigating Information-Theoretic Properties of the Typology of Spatial Demonstratives (2022)

Using data from Nintemann et al. (2020), we explore the variability in complexity and informativity across spatial demonstrative systems using spatial deictic lexicons from 223 languages. We argue from an information-theoretic perspective (Shannon, 1948) that spatial deictic lexicons are efficient in communication, balancing informativity and complexity. Specifically, we find that under an appropriate choice of cost function and need probability over meanings, among all the 21146 theoretically possible spatial deictic lexicons, those adopted by real languages lie near an efficient frontier. Moreover, we find that the conditions that the need probability and the cost function need to satisfy are consistent with the cognitive science literature regarding the source-goal asymmetry. We also show that the data are better explained by introducing a notion of systematicity, which is not currently accounted for in Information Bottleneck approaches to linguistic efficiency.
How Universal is Metonymy? Results from a Large-Scale Multilingual Analysis (2022)

Metonymy is regarded by most linguists as a universal cognitive phenomenon, especially since the emergence of the theory of conceptual mappings. However, the field data backing up claims of universality has not been large enough so far to provide conclusive evidence. We introduce a large-scale analysis of metonymy based on a lexical corpus of over 20 thousand metonymy instances from 189 languages and 69 genera. No prior study, to our knowledge, is based on linguistic coverage as broad as ours. Drawing on corpus analysis, evidence of universality is found at three levels: systematic metonymy in general, particular metonymy patterns, and specific metonymy concepts.
PaVeDa - Pavia Verbs Database: Challenges and Perspectives (2022)

This paper describes an ongoing endeavor to construct Pavia Verbs Database (PaVeDa) {--} an open-access typological resource that builds upon previous work on verb argument structure, in particular the Valency Patterns Leipzig (ValPaL) project (Hartmann et al., 2013). The PaVeDa database features four major innovations as compared to the ValPaL database: (i) it includes data from ancient languages enabling diachronic research; (ii) it expands the language sample to language families that are not represented in the ValPaL; (iii) it is linked to external corpora that are used as sources of usage-based examples of stored patterns; (iv) it introduces a new cross-linguistic layer of annotation for valency patterns which allows for contrastive data visualization.