论文标题
与模型不可解释性的跨域数据上语言模型的比较研究
Comparative Study of Language Models on Cross-Domain Data with Model Agnostic Explainability
论文作者
论文摘要
随着NLP中双向上下文化变压器语言模型的最新涌入，必须在各种数据集上对这些模型进行系统的比较研究。同样，这些语言模型的性能尚未在非磁盘数据集上探索。论文中介绍的研究比较了最先进的语言模型-Bert，Electra及其衍生物，其中包括Roberta，Albert和Distilbert。我们通过对这些模型进行跨域和不同数据进行填充进行了实验，并对模型的性能进行了深入分析。此外，提出了语言模型与预处理相一致的解释性，该模型通过模型不可知论方法验证了这些模型的上下文捕获这些模型的能力。实验结果为Yelp 2013评分分类任务和金融短语bank情绪检测任务建立了新的最新最新，精度分别为69％和88.2％的精度。最后，这里授予的研究可以极大地帮助行业研究人员在绩效或计算效率方面有效地选择语言模型。
With the recent influx of bidirectional contextualized transformer language models in the NLP, it becomes a necessity to have a systematic comparative study of these models on variety of datasets. Also, the performance of these language models has not been explored on non-GLUE datasets. The study presented in paper compares the state-of-the-art language models - BERT, ELECTRA and its derivatives which include RoBERTa, ALBERT and DistilBERT. We conducted experiments by finetuning these models for cross domain and disparate data and penned an in-depth analysis of model's performances. Moreover, an explainability of language models coherent with pretraining is presented which verifies the context capturing capabilities of these models through a model agnostic approach. The experimental results establish new state-of-the-art for Yelp 2013 rating classification task and Financial Phrasebank sentiment detection task with 69% accuracy and 88.2% accuracy respectively. Finally, the study conferred here can greatly assist industry researchers in choosing the language model effectively in terms of performance or compute efficiency.
