On the evaluation and application of neural language models for grammatical error detection

Abstract

Neural language models (NLM) have become a core component in many downstream applications within the field of natural language processing, including the task of data-driven automatic grammatical error detection (GED). This thesis explores whether information from NLMs can positively transfer to GED within the domain of learning English as a second language (ESL), and looks at whether NLMs encode and make use of linguistic signals that would facilitate robust and generalisable GED performance. First, I investigate whether information from different types of neural language model can be transferred to models for GED. I evaluate five models against three publicly available ESL benchmarks, and report results showing positive transfer effects to the extent that fine-grained error detection using a single model is becoming viable. Second, I carry out a causal investigation to understand whether NLM-GED models make use of robust linguistic signals during inference – in theory, this would enable them to generalise across different data distributions. The results show a high degree of linear encoding of noun-number within each model’s token-level contextual representations, but they also show markedly varying error detection performance across model types and across in- and out-of-domain datasets. Altogether, the results indicate models employ different strategies for error detection. Third, I re-frame the typically downstream GED task as an evaluation framework to test whether the pre-trained NLMs implicitly encode information about grammatical errors as an artefact of their language modelling objective. I present results illustrating stark differences between masked language models and autoregressive language models – while the former seemingly encodes much more information related to the detection of grammatical errors, the results also present evidence of a brittle encoding across different syntactic constructions. Altogether, this thesis presents a holistic analysis of NLMs – how they might be applied to GED, whether they utilise linguistic information to enable robust inference, and whether their pre-training objective implicitly imbues them with knowledge about grammaticality.