Interpretability Analysis for Named Entity Recognition to Understand System Predictions and How They Can Improve
Interpretability Analysis for Named Entity Recognition to Understand System Predictions and How They Can Improve
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DOI：
10.1162/coli_a_00397
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发表时间：
2021-03-01
影响因子：
9.3
通讯作者：
Nenkova, Ani
中科院分区：
文献类型：
--
作者：
Agarwal, Oshin;Yang, Yinfei;Nenkova, Ani
命名实体识别系统在英语新闻等领域取得了显著的效果。人们自然会问：这些模型实际上是在学习什么来实现这一目标？他们仅仅是在记忆自己的名字吗？或者他们能够解释文本并从语言上下文推断出正确的实体类型吗？我们通过比较命名实体识别体系结构的几种变体的性能来研究这些问题，其中一些仅提供上下文的表示作为特征。我们使用基于GloVe的BiLSTM-CRF和BERT进行实验。我们发现上下文确实会影响预测，但推动高性能的主要因素是学习命名令牌本身。此外，我们发现BERT并不总是比BiLSTM-CRF模型更好地识别预测上下文。我们招募人类注释者来评估仅从上下文推断实体类型的可行性，并发现人类也大多无法推断出仅上下文系统出错的大多数示例的实体类型。然而，还有改进的空间：系统应该能够正确识别预测上下文中的任何命名实体，我们的实验表明，当前的系统可以通过这种能力得到改进。我们的人类研究还表明，系统和人类并不总是学习相同的上下文线索，即使人类无法从上下文中识别实体类型，仅上下文系统有时也是正确的。最后，我们发现导致模型错误的一个问题是使用“纠缠”表示，将上下文和本地令牌信息编码到单个向量中，这可能会掩盖线索。我们的研究结果表明，设计模型，明确地操作表示本地输入和上下文，分别在某些情况下，可能会提高性能。鉴于这些和相关的调查结果，我们强调未来的工作方向。
Named entity recognition systems achieve remarkable performance on domains such as English news. It is natural to ask: What are these models actually learning to achieve this? Are they merely memorizing the names themselves? Or are they capable of interpreting the text and inferring the correct entity type from the linguistic context? We examine these questions by contrasting the performance of several variants of architectures for named entity recognition, with some provided only representations of the context as features. We experiment with GloVe-based BiLSTM-CRF as well as BERT. We find that context does influence predictions, but the main factor driving high performance is learning the named tokens themselves. Furthermore, we find that BERT is not always better at recognizing predictive contexts compared to a BiLSTM-CRF model. We enlist human annotators to evaluate the feasibility of inferring entity types from context alone and find that humans are also mostly unable to infer entity types for the majority of examples on which the context-only system made errors. However, there is room for improvement: A system should be able to recognize any named entity in a predictive context correctly and our experiments indicate that current systems may be improved by such capability. Our human study also revealed that systems and humans do not always learn the same contextual clues, and context-only systems are sometimes correct even when humans fail to recognize the entity type from the context. Finally, we find that one issue contributing to model errors is the use of "entangled" representations that encode both contextual and local token information into a single vector, which can obscure clues. Our results suggest that designing models that explicitly operate over representations of local inputs and context, respectively, may in some cases improve performance. In light of these and related findings, we highlight directions for future work.