Articles

AbstractTopic modeling of a text corpus is one of the most well-studied areas of information retrieval and knowledge discovery. Despite several decades of research in the area that begets an array of modeling tools, some common problems still obstruct automated topic modeling from matching users’ expectations. In particular, existing topic …...

AbstractThis paper presents a series of approaches aimed at enhancing the performance of Aspect-Based Sentiment Analysis (ABSA) by utilizing extracted semantic information from a Semantic Role Labeling (SRL) model. We propose a novel end-to-end Semantic Role Labeling model that effectively captures most of the structured semantic information within the Transformer …...

AbstractFigurative language is commonplace in natural language, and while making communication memorable and creative, can be difficult to understand. In this work, we investigate the robustness of Question Answering (QA) models on figurative text. Yes/no questions, in particular, are a useful probe of figurative language understanding capabilities of large language …...

AbstractMultiword Terms (MWTs) are domain-specific Multiword Expressions (MWE) where two or more lexemes converge to form a new unit of meaning. The task of processing MWTs is crucial in many Natural Language Processing (NLP) applications, including Machine Translation (MT) and terminology extraction. However, the automatic detection of those terms is …...

AbstractText classification is an area of research which has been studied over the years in Natural Language Processing (NLP). Adapting NLP to multiple domains has introduced many new challenges for text classification and one of them is long document classification. While state-of-the-art transformer models provide excellent results in text classification, …...

AbstractTopic models provide an efficient way of extracting insights from text and supporting decision-making. Recently, novel methods have been proposed to model topic hierarchy or temporality. Modeling temporality provides more precise topics by separating topics that are characterized by similar words but located over distinct time periods. Conversely, modeling hierarchy …...

AbstractMultilingual continual learning is important for models that are designed to be deployed over long periods of time and are required to be updated when new data becomes available. Such models are continually applied to new unseen data that can be in any of the supported languages. One challenge in …...

AbstractTopic modeling is a commonly used method for identifying and extracting topics from a corpus of documents. While several evaluation techniques, such as perplexity and topic coherence, have been developed to assess the quality of extracted topics, they fail to determine whether all topics have been identified and to what …...

AbstractHuman language is known to exhibit a nested, hierarchical structure, allowing us to form complex sentences out of smaller pieces. However, many state-of-the-art neural networks models such as Transformers have no explicit hierarchical structure in their architecture—that is, they don’t have an inductive bias toward hierarchical structure. Additionally, Transformers are …...

AbstractThe machine translation (MT) field seems to focus heavily on English and other high-resource languages. Though, low-resource MT (LRMT) is receiving more attention than in the past. Successful LRMT systems (LRMTS) should make a compelling business case in terms of demand, cost and quality in order to be viable for …...

AbstractWord sense disambiguation is an NLP task embedded in different applications. We propose to evaluate its contribution to the automatic translation of French texts into pictographs, in the context of communication between doctors and patients with an intellectual disability. Different general and/or medical language models (Word2Vec, fastText, CamemBERT, FlauBERT, DrBERT, …...

AbstractWe applied text classification methods on a corpus of product reviews we created with the help of a questionnaire. We found that for certain values, “traditional” deep neural networks like CNN can give promising results compared to the baseline. We propose some ideas to improve the results in the future. …...

AbstractIn modular dialog systems, a dialog system consists of multiple conversational agents. The task “module selection” selects the appropriate sub-dialog system for an incoming user utterance. Current models for module selection use features derived from the current user turn only, such as the utterances text or confidence values of the …...

AbstractIdentification of mentions of medical concepts in social media text can provide useful information for caseload prediction of diseases like Covid-19 and Measles. We propose a simple model for the automatic identification of the medical concept mentions in the social media text. We validate the effectiveness of the proposed model …...

AbstractThis paper describes technology developed to automatically grade students on their English spontaneous spoken language proficiency with common european framework of reference for languages (CEFR) level. Our automated assessment system contains two tasks: elicited imitation and spontaneous speech assessment. Spontaneous speech assessment is a challenging task that requires evaluating various …...

AbstractThis work proposes a new pipeline for leveraging data collected on the Stack Overflow website for pre-training a multimodal model for searching duplicates on question answering websites. Our multimodal model is trained on question descriptions and source codes in multiple programming languages. We design two new learning objectives to improve …...

AbstractData augmentation methods for neural machine translation are particularly useful when limited amount of training data is available, which is often the case when dealing with low-resource languages. We introduce a novel augmentation method, which generates new sentences by swapping objects and subjects across bisentences. This is performed simultaneously based …...

AbstractIntentionally luring readers to click on a particular content by exploiting their curiosity defines a title as clickbait. Although several studies focused on detecting clickbait titles in English articles, low-resource language like Bangla has not been given adequate attention. To tackle clickbait titles in Bangla, we have constructed the first …...

AbstractOnline reviews have become critical in informing purchasing decisions, making the detection of fake reviews a crucial challenge to tackle. Many different Machine Learning based solutions have been proposed, using various data representations such as n-grams or document embeddings. In this paper, we first explore the effectiveness of different data …...

AbstractResearch on fake reviews detection and review helpfulness prediction is prevalent, yet most studies tend to focus solely on either fake reviews detection or review helpfulness prediction, considering them separate research tasks. In contrast to this prevailing pattern, we address both challenges concurrently by employing a multi-task learning approach. We …...