A Simple and Robust Approach to Detecting Subject Verb Agreement Errors
While rule based detection of subject verb agreement (SVA) errors is sensitive to syntactic parsing errors and irregularities and exceptions to the main rules, neural sequential labelers have a tendency to overfit their training data. We observe that rule based error generation is less sensitive to syntactic parsing errors and irregularities than error detection and explore a simple, yet efficient approach to getting the best of both worlds: We train neural sequential labelers on the combination of large volumes of silver standard data, obtained through rule based error generation, and gold standard data. We show that our simple protocol leads to more robust detection of SVA errors on both in domain and out of domain data, as well as in the context of other errors and long distance dependencies; and across four standard benchmarks, the induced model on average achieves a new state of the art.
NAACL