Probing Linguistic Systematicity
This addresses the issue of model interpretability and reliability for researchers and practitioners in NLP, but it is incremental as it builds on existing evidence of non-systematic generalization.
The paper tackled the problem of whether deep natural language understanding models exhibit systematicity, finding that some systems achieve high overall performance in natural language inference despite being non-systematic.
Recently, there has been much interest in the question of whether deep natural language understanding models exhibit systematicity; generalizing such that units like words make consistent contributions to the meaning of the sentences in which they appear. There is accumulating evidence that neural models often generalize non-systematically. We examined the notion of systematicity from a linguistic perspective, defining a set of probes and a set of metrics to measure systematic behaviour. We also identified ways in which network architectures can generalize non-systematically, and discuss why such forms of generalization may be unsatisfying. As a case study, we performed a series of experiments in the setting of natural language inference (NLI), demonstrating that some NLU systems achieve high overall performance despite being non-systematic.