论文标题
JU_NLP在Hinglisheval：低资源代码混合hinglish文本的质量评估
JU_NLP at HinglishEval: Quality Evaluation of the Low-Resource Code-Mixed Hinglish Text
论文作者
论文摘要
在本文中，我们介绍了一项针对INLG 2022世代挑战（Genchal）提交的系统，该系统涉及质量评估合成的质量评估。我们实施了基于BI-LSTM的神经网络模型，以预测合成Hinglish数据集的平均评分评分和分歧分数。在我们的模型中，我们将单词嵌入式用于英语和印地语数据，以及用于Hinglish Data的热门编码。我们在平均评分评分预测任务中达到了0.11的F1分数，平均平方误差为6.0。在分歧分数预测的任务中，我们的F1得分为0.18，平均平方误差为5.0。
In this paper we describe a system submitted to the INLG 2022 Generation Challenge (GenChal) on Quality Evaluation of the Low-Resource Synthetically Generated Code-Mixed Hinglish Text. We implement a Bi-LSTM-based neural network model to predict the Average rating score and Disagreement score of the synthetic Hinglish dataset. In our models, we used word embeddings for English and Hindi data, and one hot encodings for Hinglish data. We achieved a F1 score of 0.11, and mean squared error of 6.0 in the average rating score prediction task. In the task of Disagreement score prediction, we achieve a F1 score of 0.18, and mean squared error of 5.0.
