论文标题
幸运的是，话语标记可以增强语言模型以进行情感分析
Fortunately, Discourse Markers Can Enhance Language Models for Sentiment Analysis
论文作者
论文摘要
近年来，审计的语言模型彻底改变了NLP世界，同时在各种下游任务中实现了最先进的表现。但是，在许多情况下，当稀缺标记的数据时，这些模型的性能不佳，并且预计该模型将在零或几次射击设置中执行。最近，几项工作表明，持续预处理或执行第二阶段的预处理（训练）（培训）更好地与下游任务保持一致，可以改善结果，尤其是在稀缺的数据设置中。在这里，我们建议利用情感的话语标记来生成大规模弱标记的数据，而这些数据又可用于调整语言模型以进行情感分析。广泛的实验结果表明，我们在包括金融领域在内的各种基准数据集上的方法值。代码，模型和数据可从https://github.com/ibm/tslm-discourse-markers获得。
In recent years, pretrained language models have revolutionized the NLP world, while achieving state of the art performance in various downstream tasks. However, in many cases, these models do not perform well when labeled data is scarce and the model is expected to perform in the zero or few shot setting. Recently, several works have shown that continual pretraining or performing a second phase of pretraining (inter-training) which is better aligned with the downstream task, can lead to improved results, especially in the scarce data setting. Here, we propose to leverage sentiment-carrying discourse markers to generate large-scale weakly-labeled data, which in turn can be used to adapt language models for sentiment analysis. Extensive experimental results show the value of our approach on various benchmark datasets, including the finance domain. Code, models and data are available at https://github.com/ibm/tslm-discourse-markers.
