Statistical Analysis of Machine Translation Evaluation Systems for English- Hindi Language Pair

Abstract

Background: Automatic Machine Translation (AMT) Evaluation Metrics have become popular in the Machine Translation Community in recent times. This is because of the popularity of Machine Translation engines and Machine Translation as a field itself. Translator is a very important tool to break barriers between communities especially in countries like India, where people speak 22 different languages and their many variations. With the onset of Machine Translation engines, there is a need for a system that evaluates how well these are performing. This is where Machine Translation Evaluation enters. Objective: This paper discusses the importance of Automatic Machine Translation Evaluation and compares various Machine Translation Evaluation metrics by performing Statistical Analysis on various metrics and human evaluations to find out which metric has the highest correlation with human scores. Method: The correlation between the Automatic and Human Evaluation Scores as well as the correlation between the five Automatic evaluation scores are examined at the sentence level. Moreover, a hypothesis is set up and p-values are calculated to find out how significant these correlations are. Results: The results of the statistical analysis of the scores of various metrics and human scores are shown in the form of graphs to see the trend of the correlation between the scores of Automatic Machine Translation Evaluation metrics and human scores. Conclusion: Out of the five metrics considered for the study, METEOR shows the highest correlation with human scores as compared to the other metrics.

Exploring English Long Sentence Translation Methods by Applying Natural Language Processing Techniques

“…In the recurrent neural network-based model, the context representation vector C is of fixed length, and all the information in X needs to be compressed into a fixed-length vector during the encoding process, and the information will be over-compressed if the input sequence X is of longer length [27].…”