Towards a better integration of fuzzy matches in neural machine translation through data augmentation

We identify a number of aspects that can boost the performance of Neural Fuzzy Repair (NFR), an easy-to-implement method to integrate translation memory matches and neural machine translation (NMT). We explore various ways of maximising the added value of retrieved matches within the NFR paradigm for eight language combinations, using Transformer NMT systems. In particular, we test the impact of different fuzzy matching techniques, sub-word-level segmentation methods and alignment-based features on overall translation quality. Furthermore, we propose a fuzzy match combination technique that aims to maximise the coverage of source words. This is supplemented with an analysis of how translation quality is affected by input sentence length and fuzzy match score. The results show that applying a combination of the tested modifications leads to a significant increase in estimated translation quality over all baselines for all language combinations

Coherence in Machine Translation

Coherence ensures individual sentences work together to form a meaningful document. When properly translated, a coherent document in one language should result in a coherent document in another language. In Machine Translation, however, due to reasons of modeling and computational complexity, sentences are pieced together from words or phrases based on short context windows and with no access to extra-sentential context. In this thesis I propose ways to automatically assess the coherence of machine translation output. The work is structured around three dimensions: entity-based coherence, coherence as evidenced via syntactic patterns, and coherence as evidenced via discourse relations. For the first time, I evaluate existing monolingual coherence models on this new task, identifying issues and challenges that are specific to the machine translation setting. In order to address these issues, I adapted a state-of-the-art syntax model, which also resulted in improved performance for the monolingual task. The results clearly indicate how much more difficult the new task is than the task of detecting shuffled texts. I proposed a new coherence model, exploring the crosslingual transfer of discourse relations in machine translation. This model is novel in that it measures the correctness of the discourse relation by comparison to the source text rather than to a reference translation. I identified patterns of incoherence common across different language pairs, and created a corpus of machine translated output annotated with coherence errors for evaluation purposes. I then examined lexical coherence in a multilingual context, as a preliminary study for crosslingual transfer. Finally, I determine how the new and adapted models correlate with human judgements of translation quality and suggest that improvements in general evaluation within machine translation would benefit from having a coherence component that evaluated the translation output with respect to the source text

Multi-Task Contrastive Learning for 8192-Token Bilingual Text Embeddings

We introduce a novel suite of state-of-the-art bilingual text embedding models that are designed to support English and another target language. These models are capable of processing lengthy text inputs with up to 8192 tokens, making them highly versatile for a range of natural language processing tasks such as text retrieval, clustering, and semantic textual similarity (STS) calculations. By focusing on bilingual models and introducing a unique multi-task learning objective, we have significantly improved the model performance on STS tasks, which outperforms the capabilities of existing multilingual models in both target language understanding and cross-lingual evaluation tasks. Moreover, our bilingual models are more efficient, requiring fewer parameters and less memory due to their smaller vocabulary needs. Furthermore, we have expanded the Massive Text Embedding Benchmark (MTEB) to include benchmarks for German and Spanish embedding models. This integration aims to stimulate further research and advancement in text embedding technologies for these languages

Deep interactive text prediction and quality estimation in translation interfaces

The output of automatic translation systems is usually destined for human consumption. In most cases, translators use machine translation (MT) as the first step in the process of creating a fluent translation in a target language given a text in a source language. However, there are many possible ways for translators to interact with MT. The goal of this thesis is to investigate new interactive designs and interfaces for translation. In the first part of the thesis, we present pilot studies which investigate aspects of the interactive translation process, building upon insights from Human-Computer Interaction (HCI) and Translation Studies. We developed HandyCAT, an open-source platform for translation process research, which was used to conduct two user studies: an investigation into interactive machine translation and evaluation of a novel component for post-editing. We then propose new models for quality estimation (QE) of MT, and new models for es- timating the confidence of prefix-based neural interactive MT (IMT) systems. We present a series of experiments using neural sequence models for QE and IMT. We focus upon token-level QE models, which can be used as standalone components or integrated into post-editing pipelines, guiding users in selecting phrases to edit. We introduce a strong recurrent baseline for neural QE, and show how state of the art automatic post-editing (APE) models can be re-purposed for word-level QE. We also propose an auxiliary con- fidence model, which can be attached to (I)-MT systems to use the model’s internal state to estimate confidence about the model’s predictions. The third part of the thesis introduces lexically constrained decoding using grid beam search (GBS), a means of expanding prefix-based interactive translation to general lexical constraints. By integrating lexically constrained decoding with word-level QE, we then suggest a novel interactive design for translation interfaces, and test our hypotheses using simulated editing. The final section focuses upon designing an interface for interactive post-editing, incorporating both GBS and QE. We design components which introduce a new way of interacting with translation models, and test these components in a user-study