Summary of Textual Similarity As a Key Metric in Machine Translation Quality Estimation, by Kun Sun et al.
Textual Similarity as a Key Metric in Machine Translation Quality Estimation
by Kun Sun, Rong Wang
First submitted to arxiv on: 11 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Machine learning educators can summarize this research paper abstract as follows: This study proposes “textual similarity” as a novel quality estimation (QE) metric in machine translation (MT). The authors utilize sentence transformers and cosine similarity to measure semantic closeness. Compared to traditional metrics like hter, model evaluation, and sentence probability, textual similarity shows stronger correlations with human scores on the MLQE-PE dataset. Employing generalized additive models for location, shape, and scale (GAMMs), the researchers demonstrate that textual similarity consistently outperforms other metrics across multiple language pairs in predicting human scores. Interestingly, hter fails to predict human scores in QE. The study highlights the effectiveness of textual similarity as a robust QE metric, recommending its integration with other metrics into QE frameworks and MT system training for improved accuracy and usability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding a way to measure how good machine translations are without using any reference texts. They came up with a new method called “textual similarity” that uses a type of AI model and a math formula to figure out how similar two pieces of text are. When they tested this method on some data, they found it was better at predicting what humans thought of the translations than other methods. In fact, one of those other methods actually did pretty poorly! The researchers think their new method is really useful and could be used in combination with other methods to make machine translation even better. |
Keywords
» Artificial intelligence » Cosine similarity » Machine learning » Probability » Translation
