ymoslem/acl-abstracts huggingface dataset:
Using Collaborative Filtering to Model Argument Selection (2021)

This study evaluates whether model-based Collaborative Filtering (CF) algorithms, which have been extensively studied and widely used to build recommender systems, can be used to predict which common nouns a predicate can take as its complement. We find that, when trained on verb-noun co-occurrence data drawn from the Corpus of Contemporary American-English (COCA), two popular model-based CF algorithms, Singular Value Decomposition and Non-negative Matrix Factorization, perform well on this task, each achieving an AUROC of at least 0.89 and surpassing several different baselines. We then show that the embedding-vectors for verbs and nouns learned by the two CF models can be quantized (via application of k-means clustering) with minimal loss of performance on the prediction task while only using a small number of verb and noun clusters (relative to the number of distinct verbs and nouns). Finally we evaluate the alignment between the quantized embedding vectors for verbs and the Levin verb classes, finding that the alignment surpassed several randomized baselines. We conclude by discussing how model-based CF algorithms might be applied to learning restrictions on constituent selection between various lexical categories and how these (learned) models could then be used to augment a (rule-based) constituency grammar.
Domain-Specific Japanese ELECTRA Model Using a Small Corpus (2021)

Recently, domain shift, which affects accuracy due to differences in data between source and target domains, has become a serious issue when using machine learning methods to solve natural language processing tasks. With additional pretraining and fine-tuning using a target domain corpus, pretraining models such as BERT (Bidirectional Encoder Representations from Transformers) can address this issue. However, the additional pretraining of the BERT model is difficult because it requires significant computing resources. The efficiently learning an encoder that classifies token replacements accurately (ELECTRA) pretraining model replaces the BERT pretraining method`s masked language modeling with a method called replaced token detection, which improves the computational efficiency and allows the additional pretraining of the model to a practical extent. Herein, we propose a method for addressing the computational efficiency of pretraining models in domain shift by constructing an ELECTRA pretraining model on a Japanese dataset and additional pretraining this model in a downstream task using a corpus from the target domain. We constructed a pretraining model for ELECTRA in Japanese and conducted experiments on a document classification task using data from Japanese news articles. Results show that even a model smaller than the pretrained model performs equally well.
BERT-PersNER: A New Model for Persian Named Entity Recognition (2021)

Named entity recognition (NER) is one of the major tasks in natural language processing. A named entity is often a word or expression that bears a valuable piece of information, which can be effectively employed by some major NLP tasks such as machine translation, question answering, and text summarization. In this paper, we introduce a new model called BERT-PersNER (BERT based Persian Named Entity Recognizer), in which we have applied transfer learning and active learning approaches to NER in Persian, which is regarded as a low-resource language. Like many others, we have used Conditional Random Field for tag decoding in our proposed architecture. BERT-PersNER has outperformed two available studies in Persian NER, in most cases of our experiments using the supervised learning approach on two Persian datasets called Arman and Peyma. Besides, as the very first effort to try active learning in the Persian NER, using only 30{\%} of Arman and 20{\%} of Peyma, we respectively achieved 92.15{\%}, and 92.41{\%} performance of the mentioned supervised learning experiments.
Cross-lingual Fine-tuning for Abstractive Arabic Text Summarization (2021)

While abstractive summarization in certain languages, like English, has already reached fairly good results due to the availability of trend-setting resources, like the CNN/Daily Mail dataset, and considerable progress in generative neural models, progress in abstractive summarization for Arabic, the fifth most-spoken language globally, is still in baby shoes. While some resources for extractive summarization have been available for some time, in this paper, we present the first corpus of human-written abstractive news summaries in Arabic, hoping to lay the foundation of this line of research for this important language. The dataset consists of more than 21 thousand items. We used this dataset to train a set of neural abstractive summarization systems for Arabic by fine-tuning pre-trained language models such as multilingual BERT, AraBERT, and multilingual BART-50. As the Arabic dataset is much smaller than e.g. the CNN/Daily Mail dataset, we also applied cross-lingual knowledge transfer to significantly improve the performance of our baseline systems. The setups included two M-BERT-based summarization models originally trained for Hungarian/English and a similar system based on M-BART-50 originally trained for Russian that were further fine-tuned for Arabic. Evaluation of the models was performed in terms of ROUGE, and a manual evaluation of fluency and adequacy of the models was also performed.
Behavior of Modern Pre-trained Language Models Using the Example of Probing Tasks (2021)

Modern transformer-based language models are revolutionizing NLP. However, existing studies into language modelling with BERT have been mostly limited to English-language material and do not pay enough attention to the implicit knowledge of language, such as semantic roles, presupposition and negations, that can be acquired by the model during training. Thus, the aim of this study is to examine behavior of the model BERT in the task of masked language modelling and to provide linguistic interpretation to the unexpected effects and errors produced by the model. For this purpose, we used a new Russian-language dataset based on educational texts for learners of Russian and annotated with the help of the National Corpus of the Russian language. In terms of quality metrics (the proportion of words, semantically related to the target word), the multilingual BERT is recognized as the best model. Generally, each model has distinct strengths in relation to a certain linguistic phenomenon. These observations have meaningful implications for research into applied linguistics and pedagogy, contribute to dialogue system development, automatic exercise making, text generation and potentially could improve the quality of existing linguistic technologies
Towards Quantifying Magnitude of Political Bias in News Articles Using a Novel Annotation Schema (2021)

Media bias is a predominant phenomenon present in most forms of print and electronic media such as news articles, blogs, tweets, etc. Since media plays a pivotal role in shaping public opinion towards political happenings, both political parties and media houses often use such sources as outlets to propagate their own prejudices to the public. There has been some research on detecting political bias in news articles. However, none of it attempts to analyse the nature of bias or quantify the magnitude ofthe bias in a given text. This paper presents a political bias annotated corpus viz. PoBiCo-21, which is annotated using a schema specifically designed with 10 labels to capture various techniques used to create political bias in news. We create a ranking of these techniques based on their contribution to bias. After validating the ranking, we propose methods to use it to quantify the magnitude of bias in political news articles.
Application of Mix-Up Method in Document Classification Task Using BERT (2021)

The mix-up method (Zhang et al., 2017), one of the methods for data augmentation, is known to be easy to implement and highly effective. Although the mix-up method is intended for image identification, it can also be applied to natural language processing. In this paper, we attempt to apply the mix-up method to a document classification task using bidirectional encoder representations from transformers (BERT) (Devlin et al., 2018). Since BERT allows for two-sentence input, we concatenated word sequences from two documents with different labels and used the multi-class output as the supervised data with a one-hot vector. In an experiment using the livedoor news corpus, which is Japanese, we compared the accuracy of document classification using two methods for selecting documents to be concatenated with that of ordinary document classification. As a result, we found that the proposed method is better than the normal classification when the documents with labels shortages are mixed preferentially. This indicates that how to choose documents for mix-up has a significant impact on the results.
Translation Memory Retrieval Using Lucene (2021)

Translation Memory (TM) system, a major component of computer-assisted translation (CAT), is widely used to improve human translators' productivity by making effective use of previously translated resource. We propose a method to achieve high-speed retrieval from a large translation memory by means of similarity evaluation based on vector model, and present the experimental result. Through our experiment using Lucene, an open source information retrieval search engine, we conclude that it is possible to achieve real-time retrieval speed of about tens of microseconds even for a large translation memory with 5 million segment pairs.
Now, It`s Personal : The Need for Personalized Word Sense Disambiguation (2021)

Authors of text tend to predominantly use a single sense for a lemma that can differ among different authors. This might not be captured with an author-agnostic word sense disambiguation (WSD) model that was trained on multiple authors. Our work finds that WordNet`s first senses, the predominant senses of our dataset`s genre, and the predominant senses of an author can all be different and therefore, author-agnostic models could perform well over the entire dataset, but poorly on individual authors. In this work, we explore methods for personalizing WSD models by tailoring existing state-of-the-art models toward an individual by exploiting the author`s sense distributions. We propose a novel WSD dataset and show that personalizing a WSD system with knowledge of an author`s sense distributions or predominant senses can greatly increase its performance.
Multilingual Image Corpus: Annotation Protocol (2021)

In this paper, we present work in progress aimed at the development of a new image dataset with annotated objects. The Multilingual Image Corpus consists of an ontology of visual objects (based on WordNet) and a collection of thematically related images annotated with segmentation masks and object classes. We identified 277 dominant classes and 1,037 parent and attribute classes, and grouped them into 10 thematic domains such as sport, medicine, education, food, security, etc. For the selected classes a large-scale web image search is being conducted in order to compile a substantial collection of high-quality copyright free images. The focus of the paper is the annotation protocol which we established to facilitate the annotation process: the Ontology of visual objects and the conventions for image selection and for object segmentation. The dataset is designed both for image classification and object detection and for semantic segmentation. In addition, the object annotations will be supplied with multilingual descriptions by using freely available wordnets.
ELERRANT: Automatic Grammatical Error Type Classification for Greek (2021)

In this paper, we introduce the Greek version of the automatic annotation tool ERRANT (Bryant et al., 2017), which we named ELERRANT. ERRANT functions as a rule-based error type classifier and was used as the main evaluation tool of the systems participating in the BEA-2019 (Bryant et al., 2019) shared task. Here, we discuss grammatical and morphological differences between English and Greek and how these differences affected the development of ELERRANT. We also introduce the first Greek Native Corpus (GNC) and the Greek WikiEdits Corpus (GWE), two new evaluation datasets with errors from native Greek learners and Wikipedia Talk Pages edits respectively. These two datasets are used for the evaluation of ELERRANT. This paper is a sole fragment of a bigger picture which illustrates the attempt to solve the problem of low-resource languages in NLP, in our case Greek.
Neural Machine Translation for Sinhala-English Code-Mixed Text (2021)

Code-mixing has become a moving method of communication among multilingual speakers. Most of the social media content of the multilingual societies are written in code-mixed text. However, most of the current translation systems neglect to convert code-mixed texts to a standard language. Most of the user written code-mixed content in social media remains unprocessed due to the unavailability of linguistic resource such as parallel corpus. This paper proposes a Neural Machine Translation(NMT) model to translate the Sinhala-English code-mixed text to the Sinhala language. Due to the limited resources available for Sinhala-English code-mixed(SECM) text, a parallel corpus is created with SECM sentences and Sinhala sentences. Srilankan social media sites contain SECM texts more frequently than the standard languages. The model proposed for code-mixed text translation in this study is a combination of Encoder-Decoder framework with LSTM units and Teachers Forcing Algorithm. The translated sentences from the model are evaluated using BLEU(Bilingual Evaluation Understudy) metric. Our model achieved a remarkable BLEU score for the translation.
Multilingual Multi-Domain NMT for Indian Languages (2021)

India is known as the land of many tongues and dialects. Neural machine translation (NMT) is the current state-of-the-art approach for machine translation (MT) but performs better only with large datasets which Indian languages usually lack, making this approach infeasible. So, in this paper, we address the problem of data scarcity by efficiently training multilingual and multilingual multi domain NMT systems involving languages of the {\ensuremath{\mathbf{I}}}{\ensuremath{\mathbf{n}}}{\ensuremath{\mathbf{d}}}{\ensuremath{\mathbf{i}}}{\ensuremath{\mathbf{a}}}{\ensuremath{\mathbf{n}}} {\ensuremath{\mathbf{s}}}{\ensuremath{\mathbf{u}}}{\ensuremath{\mathbf{b}}}{\ensuremath{\mathbf{c}}}{\ensuremath{\mathbf{o}}}{\ensuremath{\mathbf{n}}}{\ensuremath{\mathbf{t}}}{\ensuremath{\mathbf{i}}}{\ensuremath{\mathbf{n}}}{\ensuremath{\mathbf{e}}}{\ensuremath{\mathbf{n}}}{\ensuremath{\mathbf{t}}}. We are proposing the technique for using the joint domain and language tags in a multilingual setup. We draw three major conclusions from our experiments: (i) Training a multilingual system via exploiting lexical similarity based on language family helps in achieving an overall average improvement of {\ensuremath{\mathbf{3}}}.{\ensuremath{\mathbf{2}}}{\ensuremath{\mathbf{5}}} {\ensuremath{\mathbf{B}}}{\ensuremath{\mathbf{L}}}{\ensuremath{\mathbf{E}}}{\ensuremath{\mathbf{U}}} {\ensuremath{\mathbf{p}}}{\ensuremath{\mathbf{o}}}{\ensuremath{\mathbf{i}}}{\ensuremath{\mathbf{n}}}{\ensuremath{\mathbf{t}}}{\ensuremath{\mathbf{s}}} over bilingual baselines, (ii) Technique of incorporating domain information into the language tokens helps multilingual multi-domain system in getting a significant average improvement of {\ensuremath{\mathbf{6}}} {\ensuremath{\mathbf{B}}}{\ensuremath{\mathbf{L}}}{\ensuremath{\mathbf{E}}}{\ensuremath{\mathbf{U}}} {\ensuremath{\mathbf{p}}}{\ensuremath{\mathbf{o}}}{\ensuremath{\mathbf{i}}}{\ensuremath{\mathbf{n}}}{\ensuremath{\mathbf{t}}}{\ensuremath{\mathbf{s}}} over the baselines, (iii) Multistage fine-tuning further helps in getting an improvement of {\ensuremath{\mathbf{1}}}-{\ensuremath{\mathbf{1}}}.{\ensuremath{\mathbf{5}}} {\ensuremath{\mathbf{B}}}{\ensuremath{\mathbf{L}}}{\ensuremath{\mathbf{E}}}{\ensuremath{\mathbf{U}}} {\ensuremath{\mathbf{p}}}{\ensuremath{\mathbf{o}}}{\ensuremath{\mathbf{i}}}{\ensuremath{\mathbf{n}}}{\ensuremath{\mathbf{t}}}{\ensuremath{\mathbf{s}}} for the language pair of interest.
Fiction in Russian Translation: A Translationese Study (2021)

This paper presents a translationese study based on the parallel data from the Russian National Corpus (RNC). We explored differences between literary texts originally authored in Russian and fiction translated into Russian from 11 languages. The texts are represented with frequency-based features that capture structural and lexical properties of language. Binary classification results indicate that literary translations can be distinguished from non-translations with an accuracy ranging from 82 to 92{\%} depending on the source language and feature set. Multiclass classification confirms that translations from distant languages are more distinct from non-translations than translations from languages that are typologically close to Russian. It also demonstrates that translations from same-family source languages share translationese properties. Structural features return more consistent results than features relying on external resources and capturing lexical properties of texts in both translationese detection and source language identification tasks.
Corpus Creation and Language Identification in Low-Resource Code-Mixed Telugu-English Text (2021)

Code-Mixing (CM) is a common phenomenon in multilingual societies. CM plays a significant role in technology and medical fields where terminologies in the native language are not available or known. Language Identification (LID) of the CM data will help solve NLP tasks such as Spell Checking, Named Entity Recognition, Part-Of-Speech tagging, and Semantic Parsing. In the current era of machine learning, a common problem to the above-mentioned tasks is the availability of Learning data to train models. In this paper, we introduce two Telugu-English CM manually annotated datasets (Twitter dataset and Blog dataset). The Twitter dataset contains more romanization variability and misspelled words than the blog dataset. We compare across various classification models and perform extensive bench-marking using both Classical and Deep Learning Models for LID compared to existing models. We propose two architectures for language classification (Telugu and English) in CM data: (1) Word Level Classification (2) Sentence Level word-by-word Classification and compare these approaches presenting two strong baselines for LID on these datasets.
Sentiment Analysis in Code-Mixed Telugu-English Text with Unsupervised Data Normalization (2021)

In a multilingual society, people communicate in more than one language, leading to Code-Mixed data. Sentimental analysis on Code-Mixed Telugu-English Text (CMTET) poses unique challenges. The unstructured nature of the Code-Mixed Data is due to the informal language, informal transliterations, and spelling errors. In this paper, we introduce an annotated dataset for Sentiment Analysis in CMTET. Also, we report an accuracy of 80.22{\%} on this dataset using novel unsupervised data normalization with a Multilayer Perceptron (MLP) model. This proposed data normalization technique can be extended to any NLP task involving CMTET. Further, we report an increase of 2.53{\%} accuracy due to this data normalization approach in our best model.
From Constituency to UD-Style Dependency: Building the First Conversion Tool of Turkish (2021)

This paper deliberates on the process of building the first constituency-to-dependency conversion tool of Turkish. The starting point of this work is a previous study in which 10,000 phrase structure trees were manually transformed into Turkish from the original PennTreebank corpus. Within the scope of this project, these Turkish phrase structure trees were automatically converted into UD-style dependency structures, using both a rule-based algorithm and a machine learning algorithm specific to the requirements of the Turkish language. The results of both algorithms were compared and the machine learning approach proved to be more accurate than the rule-based algorithm. The output was revised by a team of linguists. The refined versions were taken as gold standard annotations for the evaluation of the algorithms. In addition to its contribution to the UD Project with a large dataset of 10,000 Turkish dependency trees, this project also fulfills the important gap of a Turkish conversion tool, enabling the quick compilation of dependency corpora which can be used for the training of better dependency parsers.
Making Your Tweets More Fancy: Emoji Insertion to Texts (2021)

In the social media, users frequently use small images called emojis in their posts. Although using emojis in texts plays a key role in recent communication systems, less attention has been paid on their positions in the given texts, despite that users carefully choose and put an emoji that matches their post. Exploring positions of emojis in texts will enhance understanding of the relationship between emojis and texts. We extend an emoji label prediction task taking into account the information of emoji positions, by jointly learning the emoji position in a tweet to predict the emoji label. The results demonstrate that the position of emojis in texts is a good clue to boost the performance of emoji label prediction. Human evaluation validates that there exists a suitable emoji position in a tweet, and our proposed task is able to make tweets more fancy and natural. In addition, considering emoji position can further improve the performance for the irony detection task compared to the emoji label prediction. We also report the experimental results for the modified dataset, due to the problem of the original dataset for the first shared task to predict an emoji label in SemEval2018.
Addressing Slot-Value Changes in Task-oriented Dialogue Systems through Dialogue Domain Adaptation (2021)

Recent task-oriented dialogue systems learn a model from annotated dialogues, and such dialogues are in turn collected and annotated so that they are consistent with certain domain knowledge. However, in real scenarios, domain knowledge is subject to frequent changes, and initial training dialogues may soon become obsolete, resulting in a significant decrease in the model performance. In this paper, we investigate the relationship between training dialogues and domain knowledge, and propose Dialogue Domain Adaptation, a methodology aiming at adapting initial training dialogues to changes intervened in the domain knowledge. We focus on slot-value changes (e.g., when new slot values are available to describe domain entities) and define an experimental setting for dialogue domain adaptation. First, we show that current state-of-the-art models for dialogue state tracking are still poorly robust to slot-value changes of the domain knowledge. Then, we compare different domain adaptation strategies, showing that simple techniques are effective to reduce the gap between training dialogues and domain knowledge.