Document-Level Event Argument Extraction With a Chain Reasoning Paradigm (2023)

Abstract: Document-level event argument extraction aims to identify event arguments beyond sentence level, where a significant challenge is to model long-range dependencies. Focusing on this challenge, we present a new chain reasoning paradigm for the task, which can generate decomposable first-order logic rules for reasoning. This paradigm naturally captures long-range interdependence due to the chains’ compositional nature, which also improves interpretability by explicitly modeling the reasoning process. We introduce T-norm fuzzy logic for optimization, which permits end-to-end learning and shows promise for integrating the expressiveness of logical reasoning with the generalization of neural networks. In experiments, we show that our approach outperforms previous methods by a significant margin on two standard benchmarks (over 6 points in F1).Moreover, it is data-efficient in low-resource scenarios and robust enough to defend against adversarial attacks.

## introduction identifying event arguments (i.e., participants of an event) is a crucial task for document-level event understanding @xcite . in this task, the major challenge is to model longrange dependencies between event triggers and arguments, as an event expression can span multiple sentences @xcite @xcite . consider the event expressed by a trigger detonated (@xmath0. to locate its argument tartus (semantic @xmath1, a model should capture a large context window of three sentences and 178 words to support the reasoning process. currently, it still remains an open problem for effectively capturing such dependencies @xcite . prior research has proposed to model * equal contribution. the second explosion was a sucicde bomber who detonated his belt as people rushed … … 178 words are omitted … … the arzunah bridge on a double explosion at the entrance to the city of tartus, at a … the second explosion was a sucicde bomber who detonated his belt as people rushed … … 178 words are omitted… … the arzunah bridge on a double explosion at the entrance to the city of tartus, at a … beyond-sentence clues by incorporating hierarchical encoding mechanisms @xcite , generative paradigms @xcite @xcite , and document-level inductive bias @xcite @xcite . nevertheless, such methods do not explicitly characterize the reasoning patterns underlying the document context, which potentially suffers sub-optimal performance. in addition, most previous methods are not interpretable because they rely on black-box neural networks. in this paper, we propose a new chain-ofreasoning paradigm to address document-level event argument extraction (eae). as indicated at the bottom of figure 1 , our method seeks to describe the global argument-finding process via a chain of local inference steps. for example, we may use the following chain to locate tartus: detonated ## approach figure 2 presents the overview of our approach, with an example for extracting the argument of a place role for the event detonated. let @xmath2, • • • , t, • • • , w n } be a document with n words and an event trigger t , and let rl(t , ?) be a query for the event argument of a semantic role rl. instead of directly performing the reasoning that may involve high-level processes, our approach represents the query as a fol rule with conjunctive propositions and low-level predicates {r i } n 1 : in this way, the body of the rule suggests a reasoning chain: we utilize a two-predicate formulation, specifically rl(t, ?) ← r 1 (t, b) ∧ r 2 (b, ?), to explain our method, and we describe general cases in § 4. ## optimization and generalization optimization with fol rules is typically challenging due to their discrete nature @xcite . here we present t-norm fuzzy logic for relaxation, which yields an end-to-end learning process. t-norm fuzzy logic for relaxation. t-norm fuzzy logic generalizes classical two-valued logic by admitting intermediary truth values between 1 (truth) and 0 (falsity). for our generated fol rule rl(t, a) ← r 1 (t, b) ∧ r 2 (b, a), we set the truth values of r 1 (t, b) and r 2 (b, a) to be the corresponding scores in equation ( 6 ) and ( 7 ), denoted by p 1 and p 2 respectively. then, following the łukasiewicz t-norm logic, the conjunction of two propositions corresponds to: generalization to general cases. we explain our method using a structure two-predicate structure, but it is easy to adapt it for general cases with any number of predicates. now assume a n-predicate structure. we first learn a sequence of role-predict association vectors a rl using an auto-regressive regime similar to e.q. (3) and (4). then, we re-rank and generate predicates r 1 , r 2 , • • • , r n to form the logic rule. for optimization, we drive the following metric, p(r ## experimental setups benchmarks and evaluations. we conduct experiments using two document-level eae benchmarks: rams @xcite and wikievents @xcite . the rams benchmark defines 139 event types and 59 semantic roles and gives 7,329 annotated documents; the wikievents benchmark defines 50 event types and 59 semantic roles and provides 246 annotated documents. the detailed data statistics are shown in implementations. in our approach, we use bert base to learn the contextualized word representations @xcite . the hyperparameters are tuned using the development set. finally, the size of the entity candidate set k is set to 40, selected from the range @xcite @xcite , whereas the size of the argument candidate set is determined automatically by the external entity recognizer. the number of predicates m is set to 20 out of @xcite @xcite options. for optimization, we use the adam optimizer (kingma and ba, 2015) with a batch size of 10 from @xcite @xcite and a learning rate of 1e-4 from [1e-3, 1e-4, 1e-5]. baselines. for comparison, we consider the following four categories of methods: 1) traditional approaches, such as biolabel @xcite , which views the task as a sequential labeling problem. 2) global encoding methods, such as qaee @xcite and docmrc @xcite , which form the task as a document-based question-answering problem, and memnet @xcite , which uses a memory to store global event information. 3) generative methods, such as bart-gen @xcite , which proposes a sequence-to-sequence paradigm for argument extraction, and paie @xcite , which employs a set generation formulation. 4) methods using extra supervisions, for example, feae @xcite , which adopts frame-related knowledge, and tsar @xcite , which utilizes abstract meaning representation (amr) resources. ## experimental results in this section, we present the key results, separated by the overall performance and results of capturing long-range dependencies. wikievents. in addition, we discover that the main improvement derives from improved recall, suggesting that learning the reasoning logic rule facilitates locating arguments that were difficult for previous global reasoning methods. ## discussion we conduct a series of detailed studies to further verify the effectiveness of our model. to ease discussion, we use the rams benchmark as a case. ## limitations one limitation of our method is that when there are rules of different lengths, the final result is decided by ensemble, not by building a model to generate a single rule with the best length. the second way is more natural and important because figuring out the length of the rule is also a key part of symbolic reasoning. however, it requires more parameterization (for example, the length of the rule could be a parameter) and a more advanced way to optimize. the investigation of the above method is left for future works. noise type document with noise atk1 (rand.) [s1][s2][s3]: ... their homes and paris have been damaged, burned or destroyed ... [s4][s5] atk2 (rand.) [s1][s2][s3] the answer is paris. [s4][s5] atk3 (rand.) [s1][s2][s3] the argument of the place is paris. [s4][s5] atk1 (arg.) [s1][s2][s3]: ... their homes and aleppo have been damaged ... [s4][s5] atk2 (arg.) [s1][s2][s3] the answer is aleppo. [s4][s5] atk3 (arg.) [s1][s2][s3] the argument of the place is aleppo. [s4][s5] in