Denoising Rationalization for Multi-hop Fact Verification via Multi-granular Explainer (2024)

Abstract: The success of deep learning models on multi-hop fact verification has prompted researchers to understand the behavior behind their veracity. One feasible way is erasure search: obtaining the rationale by entirely removing a subset of input without compromising verification accuracy. Despite extensive exploration, current rationalization methods struggle to discern nuanced composition within the correlated evidence, which inevitably leads to noise rationalization in multi-hop scenarios. To address this issue, this paper explores the multi-granular rationale extraction method, aiming to realize the denoising rationalization for multi-hop fact verification. Specifically, given a pretrained veracity prediction model, two independent external explainers are introduced and trained collaboratively to enhance the discriminating ability by imposing varied constraints. Meanwhile, three key properties (Fidelity, Consistency, Salience) are introduced to regularize the denoising and faithful rationalization process. Additionally, a new Noiselessness metric is proposed to measure the purity of the rationales. Experimental results on three multi-hop fact verification datasets show that the proposed approach outperforms 12 baselines.

## introduction computational multi-hop fact verification approaches typically explore neural models to verify the truthfulness of claims through multi-hop reasoning across multiple pieces of evidence @xcite . despite the prevalence of these approaches, limited attention has been given to elucidating the underlying rationale of these systems @xcite , which compromises user trust in the prediction and hinders transparency of the models @xcite . a clear-cut rationalization way to explain the behavior of a model regarding a specific prediction is erasure search @xcite @xcite , a post-hoc approache wherein the rationale is derived by searching for a maximum subset of the input (e.g., token span) that can be entirely removed without impacting the model prediction. this removal perturbation to the input guarantees the explicit decorrelation of discarded features with model prediction, thereby showing faithfulness to the model for the derived rationales @xcite . in this paper, we explore a novel denoising rationalization paradigm to extract potential post-hoc rationales for explainable multi-hop fact verification. multi-hop fact verification entails a complex reasoning scenario with distinct constituent elements as input (i.e., claim, true evidence, and noise evidence.) foot_0 @xcite . a reasonable multi-hop fact verification model ought to be capable of discerning this discrepancy by aggregating information solely from the true evidence to reach its prediction, while disregarding the noise evidence (i.e., right prediction for right reasons @xcite ). however, as depicted in fig. 1 (a), current rationalization methods follow a paradigm stemming from the explainability research on text classification tasks, which typically extract post-hoc rationales (token span) by regarding different types of evidence as equally @xcite @xcite . in this case, a consequential issue arises: extensive confusing noise token spans will inevitably be extracted as rationales from the noise evidence, i.e., noise rationalization, due to the lack of discriminating ability about the nuanced composition within the correlated evidence. this noise rationalization implies the containing of irrelevant and unfaithful rationales to the task prediction. as shown in fig. 2 , vmask @xcite ) extract token rationales from both true evidence (token spans in e1, e3, and e4) and noise evidence (token spans in e2, e5) without differentiation, while the token spans in e2 and e5 are unexpected to affect the reasoning process of the model (i.e., right prediction for wrong reasons). in essence, this rationalization uses spurious correlations between irrelevant evidence, the claim, and the inference label to extract rationales. this paper argues that a rationalization system for multi-hop fact verification is considered reasonable only when it can extract sufficient, concise, and pure rationale to reflect the basis of its decisions, which is to extract the "right tokens" from the "right sentences", as illustrated in fig. 1(b) . for example, in fig. 2 , an ideal rationalization system could solely retain the the task-relevant rationales in {e1, e3, e4} and wholly eliminate the irrelevant rationales in {e2, e5}. to tackle this issue, we propose a consistent multi-granular rationale extraction (cure) approach for denoising rationalization in multi-hop fact verification. the core idea of our work is that we introduce two independent explainers in parallel, which enables token and sentence explainers to assist and inhibit each other collaboratively by imposing varied constraints between them. this mutual effect enhances the discriminating ability of the token/sentence explainer by taking the sentence/token explainer as intermediate, thus amplifying true tokens from true evidence and suppressing noise tokens from noise evidence. in specific, given a pre-trained multi-hop fact verification model, two parameterized explainers are first trained to generate mask vectors for each claim this organism and panax are both plant genera. the gulf named after the organism is part of port mcarthur tidal wetlands system. label supports evidence e 1 (✔): [port mcarthur tidal wetlands system] the port mcarthur tidal wetlands system comprises a 994 km tract of tidal wetlands on the south-west coast of the gulf of carpentaria in the northern territory of australia. e 2 (❌): [port mcarthur tidal wetlands system] the land extends along the coast opposite the sir edward pellew group of islands, incorporating the estuaries of the mcarthur and wearyan rivers. e 3 (✔): [panax] the panax (ginseng) genus belongs to the araliaceae (ivy) family. e 4 (✔): [carpentaria] carpentaria acuminata (carpentaria palm), the sole species in the genus carpentaria, is a palm native to tropical coastal regions in the north of northern territory, australia. e 5 (❌): [sir edward pellew group of islands] the sir edward pellew group of islands is situated in the south-west corner of the gulf of carpentaria, off the northern coast of australia. ✔❌ : ground-truth sentence rationale label ✔❌ : ground-truth sentence rationale label @xcite with marked token rationales extracted by vmask @xcite . ## preliminaries task. given a claim c with associated evidence @xmath0, e 2 , . . . , e n }, we construct a fully connected input graph @xmath1, a), where n is the number of evidence, x @xmath2. we first pretrained a veracity prediction model @xcite to verify the claim, then two independent explainers are trained jointly to extract the rationales with multiple granularity, i.e., sentence rationales r @xmath3, a @xmath4, where 12594 semantic encoder ... transformer token explainer sentence explainer token mask matrix sentence mask vector token representation sentence representation c l ss l st l input graph perturbed graph ( ) f g ( ) r f g f l (a) (b) evidence inference with input inference with denoised rationale token frozen intervention gat transformer transformer aggregator ... dynamic routing evidence capsule class capsule 0.8 0.7 0.1 0.9 0.2 0.8 0.7 0.1 0.9 0.1 0.8 0.7 0.1 0.9 0.1 0.8 0.7 0.1 0.9 0.2 0.1 0.2 0.8 0.9 0.9 0.8 0.7 0.1 0.9 0.2 intersection perturbation figure 3: the overall architecture of our cure. (a) multi-granular rationale extraction with two parallel explainers to yield token and sentence mask vectors. (b) veracity prediction model with parameters fixed to verify the claim. r @xmath5,j |t i,@xmath6. finally, the valid multigranular rationale g r is obtained. definition. following jain et al. (2020), we define the faithfulness and noiselessness in a multigranular rationale extraction scenario. definition 1. (faithfulness) r s and r are multigranular faithful to their corresponding prediction y if and only if y rely entirely on g @xmath7, a s ). definition 2. (noiselessness) r s and r are multigranular noiseless if and only if satisfying ## method as shown in fig. 3 , we propose the method cure, which includes: (i) the veracity prediction module (fig. 3(b) ), (ii) multi-granular rationale extraction module (fig. 3(a) ), and the terms we optimize: (iii) the key properties, (iv) the optimization. ## experiments datasets we perform experiments on three multihop fact verification datasets, including hover @xcite , liar-plus (alhindi et al., 2018), and politihop @xcite . for hover, following @xcite , the dataset is constructed with retrieved evidence, where each claim is associated with 5 pieces of evidence. for liar-plus and politihop, we use the datasets provided in @xcite and restrict each claim associated with 10 and 5 pieces of evidence, respectively. all the datasets require multi-hop reasoning and consist of annotated sentence-level rationale and noise evidence. baselines since no other works aimed at multigranular rationale extraction, we compare cure with twelve single-granular rationale extraction methods, including eight intrinsic-based methods (i.e., pipeline in eraser (deyoung et al., 2020), information bottleneck (ib) @xcite , two-sentence selecting (tss) @xcite , learning from rationales (lr) @xcite for sentence rationale extraction. @xcite , declare @xcite , fresh @xcite , vmask (chen and ji, 2020) for token rationale extraction.) and four post hoc methods (i.e., @xcite , shap (lundberg and lee, 2017), layer integrated gradient (l-intgrad) @xcite , diffmask @xcite for token rationale extraction). ## related work a growing interest in interpretability has led to a flurry of approaches in trying to reveal the rea-claim: indigofera does not belong to the cypress family cupressaceae. the type of tree used to build james wild horse trap does not belong to another cypress family either. cunninghamites elegans is an extinct species in this other family. label: supports predicted label: supports evidence: e 1 (✔): [juniper] junipers * coniferous plants * * genus juniperus * * cypress family cupressaceae * e 2 (❌): [*] * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * e 3 (✔): [james wild horse trap] it * * location of a corral and fences used * capture wild horses * built out of dead juniper trees * e 4 (✔): [indigofera] indigofera * * large genus of over 750 species * flowering plants belonging to the family fabaceae * e 5 (✔): [cunninghamites elegans] cunninghamites elegans * * extinct conifer species * * family cupressaceae * ✔❌ extracted sentence ground-truth label * mask-out token soning behavior behind the multi-hop fact verification task. a well-studied way is to use the attention weights as attribution score to indicate the importance of a token or sentence, such as selfattention @xcite or co-attention @xcite @xcite . while this method is incapable of guaranteeing the inattention of low-score features, drawing criticism recently @xcite . another line of research focuses on perturbation-based methods. these methods explore a built-in explainer to generate the rationale by masking the unimportant language features @xcite @xcite @xcite . this way generally employs the extract-then-predict paradigm, while @xcite reveals an issue of model interlocking in such a cooperative rationalization paradigm. recently, a few studies have explored the post hoc paradigm for explanation extraction by detaching the explainer and the task model. with the parameters of the task model frozen, they focus on the external explainer to retain the key cue in input as the rationales to indicate features the task model relies on @xcite @xcite . our work falls under the scope of the post hoc paradigm, different from the prior works that only consider the single-granular rationale, we for the first time propose a novel paradigm to yield indicative token rationales by regularizing the multi-granular rationale extraction. ## conclusion we propose a novel multi-granular rationale extraction framework for denoising rationalization in multi-hop fact verification. the parallel explainers are collaboratively modeled by constraining with three diagnostic properties. a new noiselessness metric is introduced to measure the purity of the rationales. the results on three multi-hop fact verification datasets illustrate the effectiveness of our method. in the future, we will explore how to generate counterfactual explanations.