Articles

AbstractEnforcing terminology constraints is less straight-forward in neural machine translation (NMT) than statistical machine translation. Current methods, such as alignment-based insertion or the use of factors or special tokens, each have their strengths and drawbacks. We describe the current state of research on terminology enforcement in transformer-based NMT models, and …...

AbstractPerceptions and experiences of machine translation (MT) users before, during, and after their interaction with MT systems, products or services has been overlooked both in academia and in industry. Tradi-tionally, the focus has been on productivi-ty and quality, often neglecting the human factor. We propose the concept of Ma-chine Translation …...

AbstractNeural machine translation (NMT) has shown overwhelmingly good results in recent times. This improvement in quality has boosted the presence of NMT in nearly all fields of translation. Most current translation industry workflows include postediting (PE) of MT as part of their process. For many domains and language combinations, translators …...

AbstractAs part of a larger project on the use of MT in healthcare settings among migrant communities, this paper investigates if, when, how and with what (potential) challenges migrants use MT based on a survey of 201 non-native speakers of Dutch currently living in the Netherlands. Three main findings stand …...

AbstractThe use of automatic evaluation metrics to assess Machine Translation (MT) quality is well established in the translation industry. Whereas it is relatively easy to cover the word- and character-based metrics in an MT course, it is less obvious to integrate the newer neural metrics. In this paper we discuss …...

AbstractThis paper explores how students conceptualise the processes involved in human and machine translation, and how they describe the similarities and differences between them. The paper presents the results of a survey involving university students (B.A. and M.A.) taking a course on translation who filled out an online questionnaire distributed …...

AbstractThis paper presents the DataLitMT project conducted at TH Koln – University of Applied Sciences. The project develops learning resources for teaching data literacy in its translation-specific form of professional machine translation (MT) literacy to students of translation and specialised communication programmes at BA and MA levels. We discuss the …...

AbstractIt is common for websites that contain User-Generated Text (UGT) to provide an automatic translation option to reach out to their linguistically diverse users. In such scenarios, the process of translating the users’ emotions is entirely automatic with no human intervention, neither for post-editing, nor for accuracy checking. In this …...

AbstractThe aim of this article is to present a new Neural Machine Translation (NMT) from Spanish into Galician for the social media domain that was trained with a Twitter corpus. Our main goal is to outline the methods used to build the corpus and the steps taken to train the …...

AbstractThis paper describes our zero-shot approachesfor the Visual Word Sense Disambiguation(VWSD) Task in English. Our preliminarystudy shows that the simple approach of match-ing candidate images with the phrase usingCLIP suffers from the many-to-many natureof image-text pairs. We find that the CLIP textencoder may have limited abilities in captur-ing the compositionality …...

AbstractClinical Trials Reports (CTRs) contain highly valuable health information from which Natural Language Inference (NLI) techniques determine if a given hypothesis can be inferred from a given premise. CTRs are abundant with domain terminology with particular terms that are difficult to understand without prior knowledge. Thus, we proposed to use …...

AbstractIn this paper, we present our system for the textual entailment identification task as a subtask of the SemEval-2023 Task 7: Multi-evidence Natural Language Inference for Clinical Trial Data. The entailment identification task aims to determine whether a medical statement affirms a valid entailment given a clinical trial premise or …...

AbstractIn SemEval-2023 Task 1, a task of applying Word Sense Disambiguation in an image retrieval system was introduced. To resolve this task, this work proposes three approaches: (1) an unsupervised approach considering similarities between word senses and image captions, (2) a supervised approach using a Siamese neural network, and (3) …...

AbstractThis paper presents our work on the SemEval-2023 Task 10 Explainable Detection of Online Sexism (EDOS) using lexicon-based models. Our approach consists of three main steps: lexicon construction based on Pointwise Mutual Information (PMI) and Shapley value, lexicon augmentation using an unannotated corpus and Large Language Models (LLMs), and, lastly, …...

AbstractThis paper presents our system for the SemEval-2023 Task 4, which aims to identify human values behind arguments by classifying whether or not an argument draws on a specific category. Our approach leverages a second-phase pre-training method to adapt a RoBERTa Language Model (LM) and tackles the problem using a …...

AbstractIn this system paper, we describe our submission for the 11th task of SemEval2023: Learning with Disagreements, or Le-Wi-Di for short. In the task, the assumption that there is a single gold label in NLP tasks such as hate speech or misogyny detection is challenged, and instead the opinions of …...

AbstractOnline sexism is a rising issue that threatens women’s safety, fosters hostile situations, and upholds social inequities. We describe a task SemEval-2023 Task 10 for creating English-language models that can precisely identify and categorize sexist content on internet forums and social platforms like Gab and Reddit as well to provide …...

AbstractThis paper describes the submissions of the Natural Language Processing (NLP) team from the Australian Research Council Industrial Transformation Training Centre (ITTC) for Cognitive Computing in Medical Technologies to the SemEval 2023 Task 7, i.e., multi-evidence natural language inference for clinical trial data (NLI4CT). More specifically, we were working on …...

AbstractIn populous countries, pending legal cases have been growing exponentially. There is a need for developing NLP-based techniques for processing and automatically understanding legal documents. To promote research in the area of Legal NLP we organized the shared task LegalEval - Understanding Legal Texts at SemEval 2023. LegalEval task has …...

AbstractWe present the first Africentric SemEval Shared task, Sentiment Analysis for African Languages (AfriSenti-SemEval) - The dataset is available at https://github.com/afrisenti-semeval/afrisent-semeval-2023. AfriSenti-SemEval is a sentiment classification challenge in 14 African languages: Amharic, Algerian Arabic, Hausa, Igbo, Kinyarwanda, Moroccan Arabic, Mozambican Portuguese, Nigerian Pidgin, Oromo, Swahili, Tigrinya, Twi, Xitsonga, and Yorb …...