Search Results for author: Constantin Orasan

Found 27 papers, 4 papers with code

A Semi-Automated Live Interlingual Communication Workflow Featuring Intralingual Respeaking: Evaluation and Benchmarking

no code implementations LREC 2022 Tomasz Korybski, Elena Davitti, Constantin Orasan, Sabine Braun

In this paper, we present a semi-automated workflow for live interlingual speech-to-text communication which seeks to reduce the shortcomings of existing ASR systems: a human respeaker works with a speaker-dependent speech recognition software (e. g., Dragon Naturally Speaking) to deliver punctuated same-language output of superior quality than obtained using out-of-the-box automatic speech recognition of the original speech.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +4

A Semi-supervised Approach for a Better Translation of Sentiment in Dialectical Arabic UGT

no code implementations21 Oct 2022 Hadeel Saadany, Constantin Orasan, Emad Mohamed, Ashraf Tantawy

In the online world, Machine Translation (MT) systems are extensively used to translate User-Generated Text (UGT) such as reviews, tweets, and social media posts, where the main message is often the author's positive or negative attitude towards the topic of the text.

Language Modelling Machine Translation +2

Biographical: A Semi-Supervised Relation Extraction Dataset

no code implementations2 May 2022 Alistair Plum, Tharindu Ranasinghe, Spencer Jones, Constantin Orasan, Ruslan Mitkov

The dataset, which is aimed towards digital humanities (DH) and historical research, is automatically compiled by aligning sentences from Wikipedia articles with matching structured data from sources including Pantheon and Wikidata.

Knowledge Graphs named-entity-recognition +5

BLEU, METEOR, BERTScore: Evaluation of Metrics Performance in Assessing Critical Translation Errors in Sentiment-oriented Text

no code implementations TRITON 2021 Hadeel Saadany, Constantin Orasan

The adequacy of the whole process relies on the assumption that the evaluation metrics used give a reliable indication of the quality of the translation.

Machine Translation Translation

Challenges in Translation of Emotions in Multilingual User-Generated Content: Twitter as a Case Study

no code implementations20 Jun 2021 Hadeel Saadany, Constantin Orasan, Rocio Caro Quintana, Felix Do Carmo, Leonardo Zilio

In this research, we assess whether automatic translation tools can be a successful real-life utility in transferring emotion in user-generated multilingual data such as tweets.

Machine Translation Translation

TransQuest: Translation Quality Estimation with Cross-lingual Transformers

1 code implementation COLING 2020 Tharindu Ranasinghe, Constantin Orasan, Ruslan Mitkov

Recent years have seen big advances in the field of sentence-level quality estimation (QE), largely as a result of using neural-based architectures.

Transfer Learning Translation

Fake or Real? A Study of Arabic Satirical Fake News

1 code implementation RDSM (COLING) 2020 Hadeel Saadany, Emad Mohamed, Constantin Orasan

One very common type of fake news is satire which comes in a form of a news website or an online platform that parodies reputable real news agencies to create a sarcastic version of reality.

Is it Great or Terrible? Preserving Sentiment in Neural Machine Translation of Arabic Reviews

no code implementations COLING (WANLP) 2020 Hadeel Saadany, Constantin Orasan

We address this problem by fine-tuning an NMT model with respect to sentiment polarity showing that this approach can significantly help with correcting sentiment errors detected in the online translation of Arabic UGC.

Machine Translation NMT +1

TransQuest at WMT2020: Sentence-Level Direct Assessment

1 code implementation WMT (EMNLP) 2020 Tharindu Ranasinghe, Constantin Orasan, Ruslan Mitkov

This paper presents the team TransQuest's participation in Sentence-Level Direct Assessment shared task in WMT 2020.

Data Augmentation

Intelligent Translation Memory Matching and Retrieval with Sentence Encoders

no code implementations EAMT 2020 Tharindu Ranasinghe, Constantin Orasan, Ruslan Mitkov

Matching and retrieving previously translated segments from a Translation Memory is the key functionality in Translation Memories systems.

Retrieval Translation

A Survey of the Perceived Text Adaptation Needs of Adults with Autism

no code implementations RANLP 2019 Victoria Yaneva, Constantin Orasan, Le An Ha, Natalia Ponomareva

NLP approaches to automatic text adaptation often rely on user-need guidelines which are generic and do not account for the differences between various types of target groups.

Semantic Textual Similarity with Siamese Neural Networks

no code implementations RANLP 2019 Tharindu Ranasinghe, Constantin Orasan, Ruslan Mitkov

Calculating the Semantic Textual Similarity (STS) is an important research area in natural language processing which plays a significant role in many applications such as question answering, document summarisation, information retrieval and information extraction.

Information Retrieval Question Answering +3

Sentence Simplification for Semantic Role Labelling and Information Extraction

no code implementations RANLP 2019 Richard Evans, Constantin Orasan

The paper begins with our observation of challenges in the intrinsic evaluation of sentence simplification systems, which motivates the use of extrinsic evaluation of these systems with respect to other NLP tasks.

Enhancing Unsupervised Sentence Similarity Methods with Deep Contextualised Word Representations

no code implementations RANLP 2019 Tharindu Ranasinghe, Constantin Orasan, Ruslan Mitkov

Calculating Semantic Textual Similarity (STS) plays a significant role in many applications such as question answering, document summarisation, information retrieval and information extraction.

Contextualised Word Representations Information Retrieval +5

Toponym Detection in the Bio-Medical Domain: A Hybrid Approach with Deep Learning

no code implementations RANLP 2019 Alistair Plum, Tharindu Ranasinghe, Constantin Orasan

This paper compares how different machine learning classifiers can be used together with simple string matching and named entity recognition to detect locations in texts.

BIG-bench Machine Learning named-entity-recognition +3

What Makes You Stressed? Finding Reasons From Tweets

no code implementations WS 2018 Reshmi Gopalakrishna Pillai, Mike Thelwall, Constantin Orasan

Detecting stress from social media gives a non-intrusive and inexpensive alternative to traditional tools such as questionnaires or physiological sensors for monitoring mental state of individuals.

BIG-bench Machine Learning

Annotating Near-Identity from Coreference Disagreements

no code implementations LREC 2012 Marta Recasens, M. Ant{\`o}nia Mart{\'\i}, Constantin Orasan

We present an extension of the coreference annotation in the English NP4E and the Catalan AnCora-CA corpora with near-identity relations, which are borderline cases of coreference.

CLCM - A Linguistic Resource for Effective Simplification of Instructions in the Crisis Management Domain and its Evaluations

no code implementations LREC 2012 Irina Temnikova, Constantin Orasan, Ruslan Mitkov

This article presents a new linguistic resource in the form of Controlled Language (CL) guidelines for manual text simplification in the CM domain which aims to address high TC in the CM domain and produce clear messages to be used in crisis situations.

Machine Translation Management +3

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