Unify word-level and span-level tasks: NJUNLP's Participation for the WMT2023 Quality Estimation Shared Task

• URL: http://arxiv.org/abs/2309.13230v4
• Date: Mon, 11 Dec 2023 10:53:48 GMT
• Title: Unify word-level and span-level tasks: NJUNLP's Participation for the WMT2023 Quality Estimation Shared Task
• Authors: Xiang Geng, Zhejian Lai, Yu Zhang, Shimin Tao, Hao Yang, Jiajun Chen, Shujian Huang
• Abstract summary: We introduce the NJUNLP team to the WMT 2023 Quality Estimation (QE) shared task. Our team submitted predictions for the English-German language pair on all two sub-tasks. Our models achieved the best results in English-German for both word-level and fine-grained error span detection sub-tasks.
• Score: 59.46906545506715
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce the submissions of the NJUNLP team to the WMT 2023 Quality Estimation (QE) shared task. Our team submitted predictions for the English-German language pair on all two sub-tasks: (i) sentence- and word-level quality prediction; and (ii) fine-grained error span detection. This year, we further explore pseudo data methods for QE based on NJUQE framework (https://github.com/NJUNLP/njuqe). We generate pseudo MQM data using parallel data from the WMT translation task. We pre-train the XLMR large model on pseudo QE data, then fine-tune it on real QE data. At both stages, we jointly learn sentence-level scores and word-level tags. Empirically, we conduct experiments to find the key hyper-parameters that improve the performance. Technically, we propose a simple method that covert the word-level outputs to fine-grained error span results. Overall, our models achieved the best results in English-German for both word-level and fine-grained error span detection sub-tasks by a considerable margin.

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