QED : A Framework and Dataset for Explanations in Question Answering (2021)

Abstract: A question answering system that in addition to providing an answer provides an explanation of the reasoning that leads to that answer has potential advantages in terms of debuggability, extensibility, and trust. To this end, we propose QED, a linguistically informed, extensible framework for explanations in question answering. A QED explanation specifies the relationship between a question and answer according to formal semantic notions such as referential equality, sentencehood, and entailment. We describe and publicly release an expert-annotated dataset of QED explanations built upon a subset of the Google Natural Questions dataset, and report baseline models on two tasks—post- hoc explanation generation given an answer, and joint question answering and explanation generation. In the joint setting, a promising result suggests that training on a relatively small amount of QED data can improve question answering. In addition to describing the formal, language-theoretic motivations for the QED approach, we describe a large user study showing that the presence of QED explanations significantly improves the ability of untrained raters to spot errors made by a strong neural QA baseline.

## introduction question answering (qa) systems can enable efficient access to the vast amount of information that exists as text @xcite @xcite . modern neural systems have made tremendous progress in qa accuracy in recent years @xcite . however, they generally give no explanation or justification of how they arrive at an answer to a question. models that in addition to providing an answer can explain their reasoning may have significant benefits pertaining to trust and debuggability @xcite . critical questions then, are what constitutes an explanation in question answering, and how we can enable models to provide such explanations. in an effort to make progress on these questions, in this paper we (1) introduce qed, foot_0 a linguistically grounded definition of explanations for extractive qa; and (2) describe an expert-annotated corpus of qed annotations based on the natural questions @xcite dataset. the qed corpus has been released publicly. 2 figure 1 shows a qed example. given a question and a passage, qed represents an explanation as a combination of discrete, human-interpretable steps: (1) identification of a sentence implying an answer to the question, (2) identification of noun phrases in both the question and answering sentence that refer to the same thing, and (3) confirmation that the predicate in the sentence entails the predicate in the question once referential equalities are abstracted away. this choice of explanation makes use of core semantic relations-referential equality and entailment-and thus has well-understood formal properties. in addition, we found that this way of decomposing explanations has high coverage (77% on the natural questions corpus 3 ) and can be readily extended to other forms of question answering. (see section 6.) since qed decomposes the qa process into distinct subproblems, we also believe that it should enable research directions aimed at extending or improving upon extant qa systems. in what follows, we present a definition of qed explanations. we then describe the dataset of qed annotations (7638/1353 train/dev examples), including discussion of the distribution of linguistic phenomena exhibited in the data. we move to propose four potential tasks, of varying complexity, related to the qed framework, and use the qed annotations to train and evaluate different models on two of these. additionally, we describe a rater study which shows how the presence of qed explanations can help users identify errors made by an automated qa system. ## annotation definition we now describe the form of qed annotations. the treatment in this section is somewhat informal; for formal definitions see appendix a. ## qed annotations for the natural questions we now describe qed annotations over the natural questions (nq) dataset @xcite . we first describe the annotation process, then describe agreement statistics and statistics of types of referential expressions. for discussion of the assumptions we make and future extensions to qed, please see section 6.3. we focus on questions in the nq corpus that have both a passage and short answer marked by the nq annotator. we exclude examples where the passage is a table. a qed annotator was presented with a question/paragraph pair. before performing the core qed annotation, annotators first determine whether: (1) there is a valid short answer within the paragraph (note that they can overrule the original nq judgment), and there is a valid qed explanation for that answer; (2) there is a valid short answer within the paragraph, but there is no valid qed explanation for that answer; or (3) there is no valid short answer within the passage (hence the original nq annotation is judged to be an error). ten percent of all examples fell into category (3). of the remaining 90% of examples that contained a correct short answer, 77% fell into category (1), and 23% fell into category (2). three qed annotators 5 annotated 7638 training examples (5154/1702/782 in categories 1/2/3 respectively), and 1353 dev examples (1019/183/151 in categories 1/2/3), without replication. we estimate that annotators averaged approximately 2 minutes per instance. additionally, early stages of annotation consisted of regular adjudication among annotators to establish a consensus on qed's guidelines. ## tasks and models the qed data, which we release publicly, can be used as part of wide range of qa tasks and models. after discussing some of these tasks, we assess how well two recent neural architectures, one structured and one sequence-to-sequence, perform on two of them. ## rater study a major desideratum for explanation generation models is faithfulness-that is, when the explanations generated by a model truly reflect its reasoning process @xcite . one motivation for this is that when a model is wrong, faithful explanations reliably indicate the reason for the error. in the context of qa, exposing the explanations of a faithful system should improve users' ability to spot incorrect answers. we show that this is true of a faithful qed system using a rater study. ## conclusions we have described qed, a framework for explanations in question answering, and we have introduced a dataset of qed annotations. the framework is grounded in referential equality, and entailment. in addition we have described baseline models for two qed-based tasks, and a rater study utilizing qed annotations. future work should consider the development of models based on qed, especially those that provide faithful explanations, and extensions of qed beyond the single-sentence assumption. sarah wiegreffe and yuval pinter. 2019. attention is not not explanation. in proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (emnlp-ijcnlp), pages 11-20, hong kong, china. association for computational linguistics. https://doi.org/10.18653/v1/d19 -1002 tomer wolfson, mor geva, ankit gupta, matt gardner, yoav goldberg, daniel deutch, and jonathan berant. 2020. break it down: a question understanding benchmark. transactions of the association for computational linguistics, 8:183-198. https://doi.org /10.1162/tacl a 00309 zhilin yang, peng qi, saizheng zhang, yoshua bengio, william w. cohen, ruslan salakhutdinov, and christopher d. manning. 2018. hotpotqa: a dataset for diverse, explainable multi-hop question answering. in conference on empirical methods in natural language processing (emnlp). https://doi.org/10.18653/v1/d18 -1259