Dictionary-based Data Generation for Fine-Tuning Bert for Adverbial Paraphrasing Tasks

Recent advances in natural language processing technology have led to the emergence of large and deep pre-trained neural networks. The use and focus of these networks are on transfer learning. More specifically, retraining or fine-tuning such pre-trained networks to achieve state of the art performance in a variety of challenging natural language processing/understanding (NLP/NLU) tasks. In this thesis, we focus on identifying paraphrases at the sentence level using the network Bidirectional Encoder Representations from Transformers (BERT). It is well understood that in deep learning the volume and quality of training data is a determining factor of performance. The objective of this thesis is to develop a methodology for algorithmic generation of high-quality training data for paraphrasing task, an important NLU task, as well as the evaluation of the resulting training data on fine-tuning BERT to identify paraphrases. Here we will focus on elementary adverbial paraphrases, but the methodology extends to the general case. In this work, training data for adverbial paraphrasing was generated utilizing an Oxfordiii synonym dictionary, and we used the generated data to re-train BERT for the paraphrasing task with strong results, achieving a validation accuracy of 96.875%

Discourse Structure in Machine Translation Evaluation

In this article, we explore the potential of using sentence-level discourse structure for machine translation evaluation. We first design discourse-aware similarity measures, which use all-subtree kernels to compare discourse parse trees in accordance with the Rhetorical Structure Theory (RST). Then, we show that a simple linear combination with these measures can help improve various existing machine translation evaluation metrics regarding correlation with human judgments both at the segment- and at the system-level. This suggests that discourse information is complementary to the information used by many of the existing evaluation metrics, and thus it could be taken into account when developing richer evaluation metrics, such as the WMT-14 winning combined metric DiscoTKparty. We also provide a detailed analysis of the relevance of various discourse elements and relations from the RST parse trees for machine translation evaluation. In particular we show that: (i) all aspects of the RST tree are relevant, (ii) nuclearity is more useful than relation type, and (iii) the similarity of the translation RST tree to the reference tree is positively correlated with translation quality.Comment: machine translation, machine translation evaluation, discourse analysis. Computational Linguistics, 201