Machine Translation Evaluation: A Comparative Study of Different Methods

Natural language generation (NLG) is a field of study that focuses on producing machine-generated text that resembles human language. One of the key challenges in NLG is evaluating the quality of generated text, as it can be difficult to assess its accuracy and readability. In this article, we will review the state-of-the-art methods for automatic quality estimation in NLG, including their strengths and limitations.

Quality Estimation Methods

There are two main approaches to quality estimation in NLG: (1) reference-based metrics and (2) neural network-based metrics. Reference-based metrics assess the quality of generated text by comparing it to a reference standard, such as a human-written sample. Neural network-based metrics use deep learning models to learn representations of good and bad text, then predict the quality of generated text based on these representations.

Reference-Based Metrics

Reference-based metrics are widely used in NLG because they are easy to implement and provide a clear comparison between generated and reference text. The most common reference-based metrics are:

1. BLEU (Bilingual Evaluation Understudy): measures the quality of generated text by comparing it to a reference translation. BLEU assigns a score based on the number of matching n-grams (sequences of n words) in both the generated and reference texts.
2. METEOR (Metric for Evaluation of Translation with Explicit ORdering): measures the quality of generated text by comparing it to a reference translation, while also considering the order of words in the sentence. METEOR assigns a score based on the number of matching n-grams and the similarity of the word orders between the generated and reference texts.
3. ROUGE (Recall-Oriented Understudy for Gisting Evaluation): measures the quality of generated text by comparing it to a reference summary. ROUGE assigns a score based on the number of matching n-grams in both the generated and reference summaries.

Neural Network-Based Metrics

Neural network-based metrics are becoming increasingly popular in NLG because they can learn complex representations of good and bad text. The most common neural network-based metrics are:

1. Prism: measures the quality of generated text by predicting the probability of it being correct. Prism uses a transformer-based model to learn representations of good and bad text, then predicts the quality of generated text based on these representations.
2. COMET (ComET): measures the quality of generated text by predicting the probability of it being correct, while also considering the order of words in the sentence. COMET uses a transformer-based model to learn representations of good and bad text, then predicts the quality of generated text based on these representations.

Strengths and Limitations

Both reference-based and neural network-based metrics have their strengths and limitations. Reference-based metrics are easy to implement and provide a clear comparison between generated and reference text, but they can be biased towards the reference standard. Neural network-based metrics can learn complex representations of good and bad text, but they require large amounts of training data and can be computationally expensive.

Conclusion

Automatic quality estimation is an essential component of NLG, as it allows for the evaluation and improvement of machine-generated text. Reference-based and neural network-based metrics are the two main approaches to quality estimation in NLG, each with their strengths and limitations. By understanding these methods and their applications, researchers and developers can improve the quality of machine-generated text and better understand its limitations.