From Handcrafted Features to LLMs: A Brief Survey for Machine Translation Quality Estimation
Machine Translation Quality Estimation (MTQE) is the task of estimating the quality of
machine-translated text in real time without the need for reference translations, which is of …
machine-translated text in real time without the need for reference translations, which is of …
Crossqe: Hw-tsc 2022 submission for the quality estimation shared task
Quality estimation (QE) is a crucial method to investigate automatic methods for estimating
the quality of machine translation results without reference translations. This paper presents …
the quality of machine translation results without reference translations. This paper presents …
Knowledge-prompted estimator: A novel approach to explainable machine translation assessment
Cross-lingual Machine Translation (MT) quality estimation plays a crucial role in evaluating
translation performance. GEMBA, the first MT quality assessment metric based on Large …
translation performance. GEMBA, the first MT quality assessment metric based on Large …
Partial could be better than whole. hw-tsc 2022 submission for the metrics shared task
In this paper, we present the contribution of HW-TSC to WMT 2022 Metrics Shared Task. We
propose one reference-based metric, HWTSC-EE-BERTScore*, and four referencefree …
propose one reference-based metric, HWTSC-EE-BERTScore*, and four referencefree …
CSNet: Contrastive Siamese Network for Robust SLU
Automatic speech recognition (ASR) results based on clean references are much more
accurate than those based on ASR transcripts in spoken language understanding (SLU) …
accurate than those based on ASR transcripts in spoken language understanding (SLU) …