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Deep Learning based Semantic Textual Similarity Metric for Applications in Translation Technology

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Deep Learning based Semantic Textual Similarity Metric for Applications in Translation Technology

This repository contains the LaTeX source for the PhD thesis written by Tharindu Ranasinghe. Code for running the experiments presented in the thesis can be found in each respective chapter. Parts of this thesis appeared in the following peer-reviewed papers. For many of the topics discussed in both, the thesis provides more extensive coverage with more datasets. Code for running the experiments presented in these publications can be found in each respective publication's repository, linked below.

Publications

An Exploratory Analysis of Multilingual Word-Level Quality Estimation with Cross-Lingual Transformers
Tharindu Ranasinghe, Constantin Orasan, and Ruslan Mitkov
ACL 2021
Paper Code
TransQuest: Translation Quality Estimation with Cross-lingual Transformers
Tharindu Ranasinghe, Constantin Orasan, and Ruslan Mitkov
COLING 2020
Paper Code
TransQuest at WMT2020: Sentence-Level Direct Assessment
Tharindu Ranasinghe, Constantin Orasan, and Ruslan Mitkov
WMT 2020
Paper Code
Intelligent Translation Memory Matching and Retrieval with Sentence Encoders
Tharindu Ranasinghe, Constantin Orasan, and Ruslan Mitkov
EAMT 2020
Paper Code
Semantic textual similarity based on deep learning: Can it improve matching and retrieval for Translation Memory tools?
Tharindu Ranasinghe, Ruslan Mitkov, Constantin Orasan, and Rocío Caro Quintana
Corpora in Translation and Contrastive Research in the Digital Age: Recent advances and explorations
John Benjamins
Paper Code
Semantic Textual Similarity with Siamese Neural Networks
Tharindu Ranasinghe, Constantin Orasan, and Ruslan Mitkov
RANLP 2019
Paper Code
Enhancing Unsupervised Sentence Similarity Methods with Deep Contextualised Word Representations
Tharindu Ranasinghe, Constantin Orasan, and Ruslan Mitkov
RANLP 2019
Paper Code

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Deep Learning based Semantic Textual Similarity Metric for Applications in Translation Technology

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