Spoken Language Translation Graphs Re-decoding using Automatic Quality Assessment

International audienceThis paper investigates how automatic quality assessment of spoken language translation (SLT) can help re-decoding SLT output graphs and improving the overall speech translation performance. Using robust word confidence measures (from both ASR and MT) to re-decode the SLT graph leads to a significant BLEU improvement (more than 2 points) compared to our SLT baseline (French-English task)

Understanding and Enhancing the Use of Context for Machine Translation

To understand and infer meaning in language, neural models have to learn complicated nuances. Discovering distinctive linguistic phenomena from data is not an easy task. For instance, lexical ambiguity is a fundamental feature of language which is challenging to learn. Even more prominently, inferring the meaning of rare and unseen lexical units is difficult with neural networks. Meaning is often determined from context. With context, languages allow meaning to be conveyed even when the specific words used are not known by the reader. To model this learning process, a system has to learn from a few instances in context and be able to generalize well to unseen cases. The learning process is hindered when training data is scarce for a task. Even with sufficient data, learning patterns for the long tail of the lexical distribution is challenging. In this thesis, we focus on understanding certain potentials of contexts in neural models and design augmentation models to benefit from them. We focus on machine translation as an important instance of the more general language understanding problem. To translate from a source language to a target language, a neural model has to understand the meaning of constituents in the provided context and generate constituents with the same meanings in the target language. This task accentuates the value of capturing nuances of language and the necessity of generalization from few observations. The main problem we study in this thesis is what neural machine translation models learn from data and how we can devise more focused contexts to enhance this learning. Looking more in-depth into the role of context and the impact of data on learning models is essential to advance the NLP field. Moreover, it helps highlight the vulnerabilities of current neural networks and provides insights into designing more robust models.Comment: PhD dissertation defended on November 10th, 202

Interactive and Adaptive Neural Machine Translation

In this dissertation, we examine applications of neural machine translation to computer aided translation, with the goal of building tools for human translators. We present a neural approach to interactive translation prediction (a form of "auto-complete" for human translators) and demonstrate its effectiveness through both simulation studies, where it outperforms a phrase-based statistical machine translation approach, and a user study. We find that about half of the translators in the study are faster using neural interactive translation prediction than they are when post-editing output of the same underlying machine translation system, and most translators express positive reactions to the tool. We perform an analysis of some challenges that neural machine translation systems face, particularly with respect to novel words and consistency. We experiment with methods of improving translation quality at a fine-grained level to address those challenges. Finally, we bring these two areas -- interactive and adaptive neural machine translation -- together in a simulation that shows that their combination has a positive impact on novel word translation and other metrics

Low-Resource Unsupervised NMT:Diagnosing the Problem and Providing a Linguistically Motivated Solution

Unsupervised Machine Translation hasbeen advancing our ability to translatewithout parallel data, but state-of-the-artmethods assume an abundance of mono-lingual data. This paper investigates thescenario where monolingual data is lim-ited as well, finding that current unsuper-vised methods suffer in performance un-der this stricter setting. We find that theperformance loss originates from the poorquality of the pretrained monolingual em-beddings, and we propose using linguis-tic information in the embedding train-ing scheme. To support this, we look attwo linguistic features that may help im-prove alignment quality: dependency in-formation and sub-word information. Us-ing dependency-based embeddings resultsin a complementary word representationwhich offers a boost in performance ofaround 1.5 BLEU points compared to stan-dardWORD2VECwhen monolingual datais limited to 1 million sentences per lan-guage. We also find that the inclusion ofsub-word information is crucial to improv-ing the quality of the embedding