Translation Quality with METEOR Score
Text Generation & NLP Evaluation DS practice problem on Onlearn.
Difficulty: medium.
Topics: Translation Quality with METEOR Score, WordNet Synonymy, Stemming and Lemmatization, Precision-Recall Harmonic Mean, Unigram Matching, Penalty Function Calculation, Natural Language Processing, Information Retrieval, Statistical Learning Theory, Computational Linguistics, Software Engineering for ML, Machine Translation Evaluation, String Similarity Metrics, Lexical Semantics, Alignment Algorithms, Corpus Linguistics.
Develop a function to compute the METEOR score for evaluating machine translation quality. Given a reference translation and a candidate translation, calculate the score based on unigram matches, precision, recall, F mean, and a penalty for word order fragmentation.