Can NLI Models Verify QA Systems’ Predictions? (2021)

Abstract: To build robust question answering systems, we need the ability to verify whether answers to questions are truly correct, not just “good enough” in the context of imperfect QA datasets. We explore the use of natural language inference (NLI) as a way to achieve this goal, as NLI inherently requires the premise (document context) to contain all necessary information to support the hypothesis (proposed answer to the question). We leverage large pre-trained models and recent prior datasets to construct powerful question conversion and decontextualization modules, which can reformulate QA instances as premise-hypothesis pairs with very high reliability. Then, by combining standard NLI datasets with NLI examples automatically derived from QA training data, we can train NLI models to evaluate QA models’ proposed answers. We show that our approach improves the confidence estimation of a QA model across different domains, evaluated in a selective QA setting. Careful manual analysis over the predictions of our NLI model shows that it can further identify cases where the QA model produces the right answer for the wrong reason, i.e., when the answer sentence cannot address all aspects of the question.

## introduction recent question answering systems perform well on benchmark datasets @xcite @xcite , but these models often lack the ability to verify whether an answer is correct or not; they can correctly reject some unanswerable questions @xcite @xcite , but are not always well-calibrated to spot spurious answers under distribution shifts @xcite . natural language inference (nli) @xcite suggests one way to address this shortcoming: logical entailment provides a more rigorous ## experimental settings our experiments seek to validate the utility of nli for verifying answers primarily under distribution shifts, following recent work on selective question answering @xcite . we transfer an nq-trained qa model to a range of datasets and evaluate whether nli improves answer confidence. ## improving qa calibration with nli in this section, we explore to what extent either off-the-shelf or qa-augmented nli models work as verifiers across a range of qa datasets. ## effectiveness of the proposed pipeline we present an ablation study on our pipeline to see how each component contributes to the final performance. for simplicity, we use the off-the-shelf mnli model since it does not involve training using the data generated through the pipeline. figure 5 shows the average results across five datasets and figure 6 presents individual performance on three datasets. we see that both the question converter and the decontextualizer contribute to the performance of the mnli model. in both figures, removing either module harms the performance for all datasets. on nq and bioasq, using the full context is better than the decontextualized sentence, which hints that there are cases where the full context provides necessary information. we have a more comprehensive analysis in section 6.2. moreover, we see that mnli outperforms the base qa posteriors on squad2.0 and squadadv. figure 6(a ) also shows that the largest gap between the qa and nli model is on nq, which is unsurprising since the qa model is trained on nq. these results show how the improvement in the last section is achieved: the complementary strengths of mnli and nq datasets lead to the best overall performance. ## understanding the behavior of nq-nli we perform manual analysis on 300 examples drawn from nq, triviaqa, and squad2.0 datasets where nq-nli+mnli model produced an error. we classify errors into one of 7 classes, described in section 6.1 and 6.2. all of the authors of this paper conducted the annotation. the annotations agree with a fleiss' kappa value of 0.78, with disagreements usually being between closely related categories among our 7 error classes, e.g., annotation error vs. span shifting, wrong context vs. insufficient context, as we will see later. the breakdown of the errors in each dataset is shown in ## conclusion this work presents a strong pipeline for converting qa examples into nli examples, with the intent of verifying the answer with nli predictions. the answer to the question posed in the title is yes (nli models can validate these examples), with two caveats. first, it is helpful to create qa-specific data for the nli model. second, the information that is sufficient for a question to be fully answered may not align with annotations in the qa dataset. we encourage further explorations of the interplay between these tasks and careful analysis of the predictions of qa models.