GLAF : Global-to-Local Aggregation and Fission Network for Semantic Level Fact Verification (2022)

Abstract: Accurate fact verification depends on performing fine-grained reasoning over crucial entities by capturing their latent logical relations hidden in multiple evidence clues, which is generally lacking in existing fact verification models. In this work, we propose a novel Global-to-Local Aggregation and Fission network (GLAF) to fill this gap. Instead of treating entire sentences or all semantic elements within them as nodes to construct a coarse-grained or unstructured evidence graph as in previous methods, GLAF constructs a fine-grained and structured evidence graph by parsing the rambling sentences into structural triple-level reasoning clues and regarding them as graph nodes to achieve fine-grained and interpretable evidence graph reasoning. Specifically, to capture latent logical relations between the clues, GLAF first employs a local fission reasoning layer to conduct fine-grained multi-hop reasoning, and then uses a global evidence aggregation layer to achieve information sharing and the interchange of evidence clues for final claim label prediction. Experimental results on the FEVER dataset demonstrate the effectiveness of GLAF, showing that it achieves the state-of-the-art performance by obtaining a 77.62% FEVER score.

## introduction the classic fact verification (fv) task is defined as retrieving relevant sentences as evidence and conducting joint reasoning over these evidence sentences to verify the correctness of a claim, and finally returning a result such as "supports", "refutes", or "not enough info". with the increasingly frequent internet fraud, political rumors, fake news and other false information online, fact verification is becoming more and more important. how to automatically verify the fake claims and prevent their spread is a vital problem. los angeles county, officially the county of los angeles, is the most populous county in the usa. ## model description given a claim c and l evidence sentences, the fact verification task aims to check the veracity of the claim and return a prediction label y, where @xmath0, "refutes", "not enough info"}. instead of treating entire evidence sentences or all semantic elements within them as nodes to construct a coarse-grained or unstructured evidence graph as in previous methods, glaf constructs a fine-grained and structured evidence graph g by using an off as shown in figure 2 , glaf mainly includes four modules: global semantic encoder (gse), local fission reasoning (lfr) layer, global evidence aggregation (gea) layer, and graph pooling and mlp classification layer. specifically, gse is used to obtain initial representations for the claim c and all the nodes in g; lfr, with the initial node representations as inputs, is responsible for conducting 2-hop object reasoning for fine-grained claim verification. lfr outputs updated node representations, which later will be aggregated to serve as the inputs of the gea layer; gea is utilized to achieve information sharing by performing 1order neighborhood information integration; then, graph pooling is utilized to filter noise data to select valuable nodes by calculating the evidence selection probability p e i |g ; finally, an mlp layer is used to calculate the fine-grained label prediction probability p y|e i , g . note that lfr and gea each can perform 2hop reasoning and 1-order neighborhood aggregation, respectively. by iteratively execute lfr and gea, we can implement more hop reasoning and higher order aggregation to capture sufficient logical relation for more accurate verification. such an iterative execution process is illustrated in algorithm 1 (lines 21-32). we use t to denote the iteration index and assume that 0 ≤ @xmath1, where n represents the total number of iterations and is a model parameter. for convenience, in the following discussion, we use the superscript t to denote the representations or values at the t-th iteration. next, we detail the separate modules. ## conclusion in this paper, we introduce a fresh perspective to revisit the fact verification task and propose a novel global-to-local aggregation and fission network (glaf) to capture latent logical relations hidden in evidence clues for more accurate fact verification. instead of treating evidence as sentence-level or unstructured representations as in previous work, the proposed glaf model first parses the evidence sentences as triple-level evidence clues, and then feeds them into contextual language model to obtain global semantic representations. moreover, to capture latent logical relations between the clues, glaf respectively employs a local fission layer to conduct fine-grained multi-hop reasoning, as well as a global aggregation layer to conduct interchanging of evidence clues in the graph. experimental results on the benchmark dataset fever have demonstrated the effectiveness and superior performance of our model in both overall evaluation and ablation study.