Статьи

AbstractThis case study presents a Multilingual e-commerce Project, which principal aim is to create an improved system that translates product titles and descriptions, plus other content in multiple languages. The project consisted of two main phases; a research-intensive solution using state-of-the-art Machine Translation systems and baseline language models for two …...

This paper introduces the Cross-lingual Fact Extraction and VERification (XFEVER) dataset designed for benchmarking the fact ver- ification models across different languages. We constructed it by translating the claim and ev- idence texts of the Fact Extraction and VERi- fication (FEVER) dataset released by Thorne et al. (2018) into six …...

AbstractThe GAMETRAPP project, funded by Spanish Ministry for Science and Inno-vation, aims to facilitate industry profes-sionals training on full post-editing of neural machine translation by means of a gamified environment....

AbstractSignON (https://signon-project.eu/) is a Horizon 2020 project, running from 2021 until the end of 2023, which addresses the lack of technology and services for the automatic translation between sign languages (SLs) and spoken languages, through an inclusive, human-centric solution, hence contributing to the repertoire of communication media for deaf, hard …...

Previous work has demonstrated that multilin- gual fine-tuning of a pretrained multilingual speech representation model can lead to im- proved speech recognition accuracy when there is extremely little target language data avail- able. In this paper we show that fine-tuning on labeled speech data from multiple languages sharing common phonological …...

Meeting summarization aims to distill meaningful information from lengthy meeting transcripts into concise texts, allowing participants to grasp key points quickly. However, meeting transcripts often feature complex dialogue structures, such as incomplete sentences and information scattered across multiple utterances. Additionally, the length of these transcripts often exceeds the maximum input …...

AbstractIn this paper, we introduce a workflow that utilizes human-in-the-loop for post-editing anonymized texts, with the aim of reconciling the competing needs of data privacy and data quality. By combining the strengths of machine translation and human post-editing, our methodology facilitates the efficient and effective translation of anonymized texts, while …...

A cancer registry is a critical database for cancer research, which require diverse domain knowledge and manual extraction of vital information from patient records for surveillance. In order to building a real-time and high-quality cancer registry database, a named entity recognition (NER) model based on bidirectional long short-term memory (BiLSTM)-conditional …...

In this research, we investigated GPT-4 as a question-answering model for the Holy Quran. As a first step, we built the Quran ques- tion–answer pair (QUQA) dataset, comprising 2,189 questions, and made it freely available via our repository. This dataset was then used to benchmark the performance of the current …...

This study focuses on speaker separation, investigating how to successfully separate mixed multiple speech signals. We build upon the efficient end-to-end speech separation model SuDoRM-RF and integrate the Residual Conformer Block from the MANNER model along with the Multi-view Attention block to create the efficient speech separation model ESC MA-SD …...

The existing method of using large pre-trained models with prompts for zero-shot text classifi- cation possesses powerful representation abil- ity and scalability. However, its commercial availability is relatively limited. The approach of employing class labels and existing datasets to fine-tune smaller models for zero-shot classi- fication is comparatively straightforward, yet …...

AbstractWe describe the High Performance Language Technologies project (HPLT), a 3-year EU-funded project started in September 2022. HPLT will build a space combining petabytes of natural language data with large-scale model training. It will derive monolingual and bilingual datasets from the Internet Archive and CommonCrawl and build efficient and solid …...

Automatic generation of questions about the given context is useful for the adaptation of question-answering systems or to support edu- cation. We trained and evaluated a model that generates a question in the Slovak language. We have designed an automatic metric where an additional question-answering model is used to evaluate …...

Zhangzhou Southern Min is theoretically assumed to have 7320 possible syllables but more than 70% of them are not attested in the empirical data, implying that substantial constraints have been governing the segmental sequencing and segmental-suprasegmental alignment. This study explores phonotactic constraints on syllable onsets. It addresses two important issues …...

In this study, we assess ChatGPT, OpenAI’s latest conversational chatbot and large lan- guage model (LLM), on its performance in elementary-grade arithmetic and logic prob- lems. Despite its impressive coherence in nat- ural language processing and ability to follow instructions, our findings indicate that Chat- GPT still has room for …...

The objective of Sentence-level Revision (SentRev) is to enhance the fluency of En- glish writing; however, the performance of the three baseline methods is notably suboptimal. In this study, we propose a method utilizing neural reinforcement learning, tailored to the specific character- istics of this task, which has resulted in …...

Contemporary large language mod- els (LLMs) have made significant advancements, capable of generating fluent conversations with humans and accomplishing various tasks such as programming and question answering (QA). Nevertheless, current LLMs are still faced with numerous challenges, including generating hallucinations, lacking the latest information, suffering from biases, and others. In …...

AbstractThis work proposes an approach to use Part-Of-Speech (POS) information to automatically detect context-dependent Translation Units (TUs) from a Translation Memory database pertaining to the customer support domain. In line with our goal to minimize context-dependency in TUs, we show how this mechanism can be deployed to create new gender-neutral …...

In this paper, we explore compact convolutional neural networks (CNNs) for end-to-end key- word spotting from raw audio to final recogni- tion results, without using traditional feature extraction based on spectrogram. Such fully CNN models reach 90.5% accuracy, an im- provement of 12.15% over traditional meth- ods with similar structures, …...

While argument mining has garnered attention over the years, its application in the financial sector remains nascent. This study presents a BERT-based ensemble learning approach tailored for sentiment analysis grounded in financial narratives, specifically focusing on unearthing arguments. For a nuanced analysis, we dissect the challenge into two pivotal subtasks …...