  Machine Translation (MT) Quality Estimation (QE) assesses translation
reliability without reference texts. This study introduces "textual similarity"
as a new metric for QE, using sentence transformers and cosine similarity to
measure semantic closeness. Analyzing data from the MLQE-PE dataset, we found
that textual similarity exhibits stronger correlations with human scores than
traditional metrics (hter, model evaluation, sentence probability etc.).
Employing GAMMs as a statistical tool, we demonstrated that textual similarity
consistently outperforms other metrics across multiple language pairs in
predicting human scores. We also found that "hter" actually failed to predict
human scores in QE. Our findings highlight 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.
