This paper describes the system description for the HinglishEval challenge at
INLG 2022. The goal of this task was to investigate the factors influencing the
quality of the code-mixed text generation system. The task was divided into two
subtasks, quality rating prediction and annotators disagreement prediction of
the synthetic Hinglish dataset. We attempted to solve these tasks using
sentence-level embeddings, which are obtained from mean pooling the
contextualized word embeddings for all input tokens in our text. We
experimented with various classifiers on top of the embeddings produced for
respective tasks. Our best-performing system ranked 1st on subtask B and 3rd on
subtask A.

该论文描述了 INLG 2022 HinglishEval 挑战的系统描述。该挑战旨在研究影响混合代码文本生成系统质量的因素。任务分为两个子任务，即合成 Hinglish 数据集的质量评级预测和注释者不一致预测。我们尝试使用句子级嵌入解决这些任务，这是通过对我们文本中所有输入标记的上下文化词嵌入进行平均池化获得的。我们在产生各自任务的嵌入上尝试了各种分类器。我们最有效的系统在子任务 B 上排名第一，在子任务 A 上排名第三。

niksss at HinglishEval：基于 BERT 的通用语境嵌入与 Catboost 用于低资源合成跨语混合 Hinglish 文本的质量评估

niksss at HinglishEval: Language-agnostic BERT-based Contextual Embeddings with Catboost for Quality Evaluation of the Low-Resource Synthetically Generated Code-Mixed Hinglish Text