Articles

AbstractSemantic textual similarity (STS) systems estimate the degree of the meaning similarity between two sentences. Cross-lingual STS systems estimate the degree of the meaning similarity between two sentences, each in a different language. State-of-the-art algorithms usually employ a strongly supervised, resource-rich approach difficult to use for poorly-resourced languages. However, any …...

AbstractIn recent years, a number of studies have used linear models for personality prediction based on text. In this paper, we empirically analyze and compare the lexical signals captured in such models. We identify lexical cues for each dimension of the MBTI personality scheme in several different ways, considering different …...

AbstractAcquisition of multilingual training data continues to be a challenge in word sense disambiguation (WSD). To address this problem, unsupervised approaches have been proposed to automatically generate sense annotations for training supervised WSD systems. We present three new methods for creating sense-annotated corpora which leverage translations, parallel bitexts, lexical resources, …...

AbstractThis article describes research on claim verification carried out using a multiple GAN-based model. The proposed model consists of three pairs of generators and discriminators. The generator and discriminator pairs are responsible for generating synthetic data for supported and refuted claims and claim labels. A theoretical discussion about the proposed …...

AbstractFrom statistical to neural models, a wide variety of topic modelling algorithms have been proposed in the literature. However, because of the diversity of datasets and metrics, there have not been many efforts to systematically compare their performance on the same benchmarks and under the same conditions. In this paper, …...

AbstractRecent research in opinion mining proposed word embedding-based topic modeling methods that provide superior coherence compared to traditional topic modeling. In this paper, we demonstrate how these methods can be used to display correlated topic models on social media texts using SocialVisTUM, our proposed interactive visualization toolkit. It displays a …...

AbstractIn this paper, we attempt to improve upon the state-of-the-art in predicting a novel’s success by modeling the lexical semantic relationships of its contents. We created the largest dataset used in such a project containing lexical data from 17,962 books from Project Gutenberg. We utilized domain specific feature reduction techniques …...

AbstractMultiple parallel attention mechanisms that use multiple attention heads facilitate greater performance of the Transformer model for various applications e.g., Neural Machine Translation (NMT), text classification. In multi-head attention mechanism, different heads attend to different parts of the input. However, the limitation is that multiple heads might attend to the …...

AbstractMachine reading comprehension (MRC) is one of the most challenging tasks in natural language processing domain. Recent state-of-the-art results for MRC have been achieved with the pre-trained language models, such as BERT and its modifications. Despite the high performance of these models, they still suffer from the inability to retrieve …...

AbstractThe widespread presence of offensive language on social media motivated the development of systems capable of recognizing such content automatically. Apart from a few notable exceptions, most research on automatic offensive language identification has dealt with English. To address this shortcoming, we introduce MOLD, the Marathi Offensive Language Dataset. MOLD …...

AbstractWith the increasing adoption of technology, more and more systems become target to information security breaches. In terms of readily identifying zero-day vulnerabilities, a substantial number of news outlets and social media accounts reveal emerging vulnerabilities and threats. However, analysts often spend a lot of time looking through these decentralized …...

AbstractCharacter-aware neural language models can capture the relationship between words by exploiting character-level information and are particularly effective for languages with rich morphology. However, these models are usually biased towards information from surface forms. To alleviate this problem, we propose a simple and effective method to improve a character-aware neural …...

AbstractAdaptive Machine Translation purports to dynamically include user feedback to improve translation quality. In a post-editing scenario, user corrections of machine translation output are thus continuously incorporated into translation models, reducing or eliminating repetitive error editing and increasing the usefulness of automated translation. In neural machine translation, this goal may …...

AbstractThis paper presents the preliminary results of an ongoing project that analyzes the growing body of scientific research published around the COVID-19 pandemic. In this research, a general-purpose semantic model is used to double annotate a batch of 500 sentences that were manually selected from the CORD-19 corpus. Afterwards, a …...

AbstractModern deep learning models for natural language processing rely heavily on large amounts of annotated texts. However, obtaining such texts may be difficult when they contain personal or confidential information, for example, in health or legal domains. In this work, we propose a method of de-identifying free-form text documents by …...

AbstractLarge transformer models, such as BERT, achieve state-of-the-art results in machine reading comprehension (MRC) for open-domain question answering (QA). However, transformers have a high computational cost for inference which makes them hard to apply to online QA systems for applications like voice assistants. To reduce computational cost and latency, we …...

AbstractPrevious research has used linguistic features to show that translations exhibit traces of source language interference and that phylogenetic trees between languages can be reconstructed from the results of translations into the same language. Recent research has shown that instances of translationese (source language interference) can even be detected in …...

AbstractCode-mixing (CM) is a frequently observed phenomenon that uses multiple languages in an utterance or sentence. There are no strict grammatical constraints observed in code-mixing, and it consists of non-standard variations of spelling. The linguistic complexity resulting from the above factors made the computational analysis of the code-mixed language a …...

AbstractMental health is getting more and more attention recently, depression being a very common illness nowadays, but also other disorders like anxiety, obsessive-compulsive disorders, feeding disorders, autism, or attention-deficit/hyperactivity disorders. The huge amount of data from social media and the recent advances of deep learning models provide valuable means to …...

AbstractA core task in information extraction is event detection that identifies event triggers in sentences that are typically classified into event types. In this study an event is considered as the unit to measure diversity and similarity in news articles in the framework of a news recommendation system. Current typology-based …...