Can Edge Probing Tasks Reveal Linguistic Knowledge in QA Models?

15 Sep 2021  ·  Sagnik Ray Choudhury, Nikita Bhutani, Isabelle Augenstein ·

There have been many efforts to try to understand what gram-matical knowledge (e.g., ability to understand the part of speech of a token) is encoded in large pre-trained language models (LM). This is done through 'Edge Probing' (EP) tests: simple ML models that predict the grammatical properties ofa span (whether it has a particular part of speech) using only the LM's token representations. However, most NLP applications use fine-tuned LMs. Here, we ask: if a LM is fine-tuned, does the encoding of linguistic information in it change, as measured by EP tests? Conducting experiments on multiple question-answering (QA) datasets, we answer that question negatively: the EP test results do not change significantly when the fine-tuned QA model performs well or in adversarial situations where the model is forced to learn wrong correlations. However, a critical analysis of the EP task datasets reveals that EP models may rely on spurious correlations to make predictions. This indicates even if fine-tuning changes the encoding of such knowledge, the EP tests might fail to measure it.

PDF Abstract
No code implementations yet. Submit your code now

Results from the Paper


  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods


No methods listed for this paper. Add relevant methods here