  Quality Estimation (QE) systems are important in situations where it is
necessary to assess the quality of translations, but there is no reference
available. This paper describes the approach adopted by the SurreyAI team for
addressing the Sentence-Level Direct Assessment shared task in WMT23. The
proposed approach builds upon the TransQuest framework, exploring various
autoencoder pre-trained language models within the MonoTransQuest architecture
using single and ensemble settings. The autoencoder pre-trained language models
employed in the proposed systems are XLMV, InfoXLM-large, and XLMR-large. The
evaluation utilizes Spearman and Pearson correlation coefficients, assessing
the relationship between machine-predicted quality scores and human judgments
for 5 language pairs (English-Gujarati, English-Hindi, English-Marathi,
English-Tamil and English-Telugu). The MonoTQ-InfoXLM-large approach emerges as
a robust strategy, surpassing all other individual models proposed in this
study by significantly improving over the baseline for the majority of the
language pairs.
