论文标题
与联合检索和分类的粗到1个记忆匹配
Coarse-to-Fine Memory Matching for Joint Retrieval and Classification
论文作者
论文摘要
我们提出了一种新颖的端到端语言模型，用于联合检索和分类，通过粗到精细的记忆匹配学习和推断的搜索过程，将双向和交叉编码器的优势统一为单语言模型。在标准的盲验测试集的“发烧事实验证数据集”中评估，分类准确性明显高于仅依靠语言模型参数作为知识基础的方法，并接近一些仅使用单个BERT基础模型增强随着存储层增强的多模型管道系统。我们进一步证明了如何利用耦合的检索和分类来确定较低的置信度实例，并将示例性审核扩展到此设置以分析和约束模型。结果，我们的方法通过两种不同的机制更新语言模型行为的方法：可以明确更新所检索的信息，并且可以通过示例数据库进行修改模型行为。
We present a novel end-to-end language model for joint retrieval and classification, unifying the strengths of bi- and cross- encoders into a single language model via a coarse-to-fine memory matching search procedure for learning and inference. Evaluated on the standard blind test set of the FEVER fact verification dataset, classification accuracy is significantly higher than approaches that only rely on the language model parameters as a knowledge base, and approaches some recent multi-model pipeline systems, using only a single BERT base model augmented with memory layers. We further demonstrate how coupled retrieval and classification can be leveraged to identify low confidence instances, and we extend exemplar auditing to this setting for analyzing and constraining the model. As a result, our approach yields a means of updating language model behavior through two distinct mechanisms: The retrieved information can be updated explicitly, and the model behavior can be modified via the exemplar database.
