Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
Results
Showing 1 to 20 of 26 for “"neural machine translation"”.
-
Robust Neural Machine Translation
<p>This thesis aims for general robust Neural Machine Translation (NMT) that is agnostic to the test domain. NMT has achieved high quality on benchmarks with closed datasets such as WMT and NIST but can fail when the translation input contains noise due to, for example, mismatched domains or …
-
Domain adaptation for neural machine translation
… of deep learning techniques has allowed Neural Machine Translation (NMT) models to become extremely powerful, given sufficient training data and training time. However, such translation models struggle when translating text of a specific domain. A domain may consist of text on a …
-
Exploiting monolingual data for neural machine translation
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-03-01 without embargo terms
-
Learning Morphology for Open-Vocabulary Neural Machine Translation
State-of-the-art neural machine translation systems typically have low accuracy in translating rare or unseen words due to the requirement of using a fixed-size word vocabulary during training. In addition to controlling the model complexity, this limitation is also related to the difficulty of …
-
Similarity-Augmented Prediction Methods for Neural Machine Translation
Neural language models (LMs) are now the dominant approach to most tasks in natural language processing (NLP), including machine translation (MT). In spite of their success, studies have shown systematic problems in these models such as the high dispersal of probability mass across vastly many …
-
Online Adaptive Neural Machine Translation: from single- to multi-domain scenarios
… this thesis we investigate methods for deploying machine translation (MT) in real-world application scenarios related to the use of MT in computer assisted translation (CAT), where human translators post-edit MT outputs. In particular, we investigate (in chronological order) MT adaptation under …
-
From Translation to Transformation: Contact-Induced Language change in Greek via Neural Machine Translation from English
… επαφής· και σε ποιο βαθμό τα συστήματα e-Translation, Google Translate και DeepL παρουσιάζουν διακριτά μεταφραστικά προφίλ. Το υλικό της μελέτης αποτελείται από δελτία Τύπου της Ευρωπαϊκής Επιτροπής στην αγγλική γλώσσα και τις αντίστοιχες ελληνικές αποδόσεις τους, οι οποίες έχουν παραχθεί …
-
Improving Low-Resource Translation with Finite State Grammars
… to pose a problem for the development of neural machine translation systems for low-resource languages. This study develops a method for the incorporation of linguistic information into the training of neural machine translation models for low-resource languages, using morphological …
-
On internal language representations in deep learning : an analysis of machine translation and speech recognition
… has become pervasive in everyday life. Neural networks are a key component in this technology thanks to their ability to model large amounts of data. Contrary to traditional systems, models based on deep neural networks (a.k.a. deep learning) can be trained in an end-to-end fashion on …
-
A translation framework for discovering word-like units from visual scenes and spoken descriptions
… word discovery systems based on statistical machine translation (SMT) and neural machine translation (NMT). We extend the existing theoretical frameworks on unsupervised word discovery and demonstrate a class of effective models for end-to-end word discovery from image regions and spoken …
-
Efficient methods for mapping neural machine translator on FPGAs
Neural machine translation (NMT) is one of the most critical applications in natural language processing (NLP) with the main idea to convert text in one language to another language using deep neural networks. In recent year, we have seen continuous development of NMT by integrating more emerging …
-
Forecasting Energy Consumption using Sequence to Sequence Attention models
… for sensor based energy forecasting. Machine learning algorithms commonly used for energy forecasting, such as FeedForward Neural Networks, are not well-suited for interpreting the time dimensionality of a signal. Consequently, this thesis applies Sequence-to-Sequence (S2S) Recurrent …
-
Formality Style Transfer Within and Across Languages with Limited Supervision
… the expected tone of the content when taking a translation job. Computational models of natural language should consider both their meaning and style. Controlling style is an emerging research area in text rewriting and is under-investigated in machine translation. In this dissertation, we …
-
The Roles of Language Models and Hierarchical Models in Neural Sequence-to-Sequence Prediction
… of deep learning, research in many areas of machine learning is converging towards the same set of methods and models. For example, long short-term memory networks are not only popular for various tasks in natural language processing (NLP) such as speech recognition, machine translation, …
-
Speech Adaptation Modeling for Statistical Machine Translation
Spoken language translation (SLT) exists within one of the most challenging intersections of speech and natural language processing. While machine translation (MT) has demonstrated its effectiveness on the translation of textual data, the translation of spoken language remains a challenge, largely …
-
Data-Efficient Bilingual Lexicon Induction with Pretrained Language Models
… CLWEs to retrieve a small set of candidate translations and then leverage PLMs as cross-encoder rerankers for BLI (Chapter 4). Thirdly, we investigate if it is possible to prompt autoregressive large language models (LLMs) for BLI, which completely deviates from traditional mapping-based …
-
Improving Attention-based Sequence-to-sequence Models
… in various sequence-to-sequence tasks, including Neural Machine Translation (NMT), Automatic Speech Recognition (ASR) and speech synthesis, also known as Text-To-Speech (TTS). These models are often autoregressive, which leads to high modeling capacity, but also makes training difficult. The …
-
A study of the translation of sentiment in user-generated text
… competence and fi nesse. In the professional translation industry, an incorrect translation of the sentiment-carrying lexicon is considered a critical error as it can be either misleading or in some cases harmful since it misses the fundamental aspect of the source text, i.e. the author's …
-
Automatic subtitling: A new paradigm
Audiovisual Translation (AVT) is a field where Machine Translation (MT) has long found limited success mainly due to the multimodal nature of the source and the formal requirements of the target text. Subtitling is the predominant AVT type, quickly and easily providing access to the vast amounts of …
-
Adversarial Attacks on Natural Language and Speech Processing Models
… applied to attacks on generative tasks such as Neural Machine Translation and Grammatical Error Correction. Further discussions focus on the use of an automated scoring module that operates on output sequences in the perception-based framework. It is shown that if powerful LLMs (such as ChatGPT) …
Page 1 of 2