Automatic Evaluation Method
Summary
Automatic evaluation methods usually come into place where there is hard to define a ground truth (for example in machine translation) or the labeled data is very expensive (for example in co-reference resolution). This topic has been developed a lot since last decade, and showed its applicability in many different NLP tasks.
Main Ideas
The main ideas behind automatic evaluation methods (or metrics) is that there is an alternative measure A to the original measure B. The measure B requires ground truth (or labeled data) where usually measure A does not require ground truth (or a small set of it).
A few example of alternative measures are
- Entropy, it measures how well is the mapping function between two sets and serves in the cases that the mapping function is a crucial indicator of the overall performance.
- N-gram statistics, it measures how n-gram patterns in the output text are formed, which performs well as an alternative measure for text generation tasks.
The proof of applicability for automatic evaluation methods in different task usually starts with a hypothesis and requires a correlation analysis of the alternative measure with original measure.
Applications in NLP Tasks
- BLEU for Machine Translation
The most famous application of automatic evaluation is the BLEU score for machine translation which takes the matched n-gram statistics of the system output with human generated output. The most innovative idea behind BLEU is that there is no well-defined ground truth in machine translation where many outputs are acceptable, BLEU removes the needs of human grader and allows large-scale testing, which then speeds up the development of the machine translation field. It is the groundbreaking work in this area, which inspires the rest three.
- Alignment Entropy for Machine Transliteration
- ROUGE for Text Summarization
- CONE for Co-reference Resolution
