On the Challenges of Evaluating Compositional Explanations in Multi-Hop Inference: Relevance, Completeness, and Expert Ratings (2021)

Abstract: Building compositional explanations requires models to combine two or more facts that, together, describe why the answer to a question is correct. Typically, these “multi-hop” explanations are evaluated relative to one (or a small number of) gold explanations. In this work, we show these evaluations substantially underestimate model performance, both in terms of the relevance of included facts, as well as the completeness of model-generated explanations, because models regularly discover and produce valid explanations that are different than gold explanations. To address this, we construct a large corpus of 126k domain-expert (science teacher) relevance ratings that augment a corpus of explanations to standardized science exam questions, discovering 80k additional relevant facts not rated as gold. We build three strong models based on different methodologies (generation, ranking, and schemas), and empirically show that while expert-augmented ratings provide better estimates of explanation quality, both original (gold) and expert-augmented automatic evaluations still substantially underestimate performance by up to 36% when compared with full manual expert judgements, with different models being disproportionately affected. This poses a significant methodological challenge to accurately evaluating explanations produced by compositional reasoning models.

## introduction compositional inference is the high-level task of combining two or more pieces of knowledge to perform reasoning. in the context of question answering, compositional (or "multi-hop") inference typically takes the form of combining facts from a knowledge base that allow a given solver to form a complete chain-of-reasoning that moves from question to correct answer. a desirable consequence is that the facts used to assemble this chain-of-reasoning can then be taken as an inter-question: when trees are cleared from the land, what will most likely occur? answer: soil erosion ## dataset description to support our experimentation we created a resource that, for a given worldtree v2 question, provides a set of relevance ratings for the facts most likely to be in an explanation. worldtree v2 contains 4.4k questions, and its supporting knowledge base contains approximately 9k facts, meaning that exhaustively evaluating the relevance of each fact for each question would require approximately 40 million ratings, which is intractable even for this comparatively small corpus. instead, here, annotators rate a shortlist of facts most likely to be relevant to building possible explanations, producing ratings for a total of 126k facts. ## experiments: explanation-as-ranking to characterize differences in automatic evaluation when using existing gold explanations versus expert relevance ratings, we first explore performance in the explanation-as-ranking paradigm. rather than directly producing an explanation for a given question, as a stepping-stone task, explanation-asranking @xcite @xcite ) is an explanatory retrieval analogue that requires models to exhaustively rank all facts in a knowledge base such that the most relevant facts are selectively ranked to the top of the list. 2 models: we include the exhaustive bert and roberta models trained on the original worldtree gold explanations, and used in generating the shortlist for the teacher ratings, as described above. we also include a tensorflow-ranking-bert model @xcite , which combines bert embeddings directly in a pointwise learningto-rank (pasumarthi et al., 2019a) instead of classification framework, and achieves extremely strong single-model performance for large benchmark ranking tasks such as ms marco @xcite . unlike the baseline models, tfr-bert was trained on the expert-generated relevance ratings. due to the expense in evaluating this model, here we rerank only the top 100 scoring facts from the exhaustive bert model. evaluation: explanation-as-ranking performance is reported using mean average precision (map). we evaluate in three settings: (1) using the original gold worldtree explanations, (2) treating each fact the experts rated as extra-detail or higher as gold, or (3) treating each fact the experts rated as important or higher as gold. because the expert ratings are graded (0-3) rather than binary (gold/not gold), we also evaluate using normalized discounted cumulative gain (ndcg). ## experiments: whole explanations here we construct and evaluate a diverse set of generative, top-k ranking, and schema-based models that return whole (short-length) explanations to a user, in place of ranked lists. we evaluate these in terms of both relevance and completeness, comparing both automatic and manual assessments of these metrics. ## conclusion relevance performance is still undercounted: while the expert-generated relevance ratings produced in this work provide more accurate estimates of performance compared to single gold explanations when used in fully-automatic evaluations, these automatic estimates still undercount overall model performance. in our experiments we show the expert ratings primarily provide a vehicle for training better models, but that automatically evaluating relevance performance still remains a challenge, even with a large targeted increase in relevance annotation. further, annotators reported that determining relevance of single facts in isolation is challenging because it lacks the broader compositional context of the rest of the candidate explanation, suggesting ultimate limits to the utility of exhaustive annotation. measuring completeness is a major challenge: as explanations become larger, and facts become more atomic, models are afforded more opportunities to build explanations that differ from those annotated as gold. because of this, automatic completeness judgements substantially undercount true (manual) completeness by at least a factor of two across all models. alignment approaches @xcite and annotating multiple explanations @xcite have been proposed for short explanations, but are unlikely to scale well as compositionality increases. treatments similar to those that use a formal semantics or theorem proving to evaluate truth (e.g. @xcite are attractive, but are unlikely to offer generality at scale without substantial development effort. conversely, streamlined manual evaluation frameworks are becoming increasingly common for simpler generative tasks (e.g. @xcite , but it is unclear how accurately non-technical crowdworkers would perform on rating compositional completeness -and even if possible, this would raise the time and cost associated with evaluation dramatically.