A Two-Stream AMR -enhanced Model for Document-level Event Argument Extraction (2022)

Abstract: Most previous studies aim at extracting events from a single sentence, while document-level event extraction still remains under-explored. In this paper, we focus on extracting event arguments from an entire document, which mainly faces two critical problems: a) the long-distance dependency between trigger and arguments over sentences; b) the distracting context towards an event in the document. To address these issues, we propose a T wo- S tream A bstract meaning R epresentation enhanced extraction model (TSAR). TSAR encodes the document from different perspectives by a two-stream encoding module, to utilize local and global information and lower the impact of distracting context. Besides, TSAR introduces an AMR-guided interaction module to capture both intra-sentential and inter-sentential features, based on the locally and globally constructed AMR semantic graphs. An auxiliary boundary loss is introduced to enhance the boundary information for text spans explicitly. Extensive experiments illustrate that TSAR outperforms previous state-of-the-art by a large margin, with 2.54 F1 and 5.13 F1 performance gain on the public RAMS and WikiEvents datasets respectively, showing the superiority in the cross-sentence arguments extraction. We release our code in https://github.com/PKUnlp-icler/TSAR .

## introduction event argument extraction (eae) aims at identifying the entities that serve as event arguments, and predicting the roles they play in the event, which is a key step for event extraction (ee). it helps to transform the unstructured text into structured event knowledge that can be further utilized in recommendation systems @xcite , dialogue systems @xcite , and so on. most previous studies assume that the events are only figure 1 : a document from rams dataset @xcite . a transport event is triggered by shipment, with five event arguments scattering across the document. as shown in figure 1 , the origin argument u.s. can be identified more easily without sentence 4, which does not offer useful information for the event, but contains many place entities that can be distracting, like saudi arabia and russia or iran. it remains challenging to pinpoint the useful context while discarding the distracting information. recently, @xcite use a taggingbased method, which fails to handle nested arguments. instead, span-based methods predict argument roles for candidate spans @xcite . some studies directly generate arguments based on sequence-to-sequence model @xcite . however, how to model long-distance dependency among trigger and arguments, and how to handle distracting context explicitly, remain largely under-explored. in this paper, to tackle the aforementioned two problems, we propose a two-stream amrenhanced extraction model (tsar). in order to take advantage of the essential context in the document, and avoid being misled by distractions, we introduce a two-stream encoding module. it consists of a global encoder that encodes global semantics with as much context as possible to gather adequate context information, and a local encoder that focuses on the most essential information and prudently takes in extra context. in this way, tsar can leverage complementary advantages of different encoding perspectives, and therefore make better use of the feasible context to benefit the extraction. besides, to model the long-distance dependency, we introduce an amr-guided interaction module. abstract meaning representation @xcite graph contains rich hierarchical semantic relations among different concepts, which makes it favorable for complex event extraction. from such a linguistic-driven angle, we turn the linear structure of the document into both global and local graph structures, followed by a graph neural network to enhance the interactions, especially for those non-local elements. finally, as tsar extracts arguments in span level, where the span boundary may be ambiguous, we introduce an auxiliary boundary loss to enhance span representation with calibrated boundary. to summarize, our contributions are three-fold. 1) we propose a two-stream encoding module for document-level eae, which encodes the document through two different perspectives to better utilize the context. 2) we introduce an amr-guided in-teraction module to facilitate the semantic interactions within the document, so that long-distance dependency can be better captured. 3) our experiments show that tsar outperforms the previous start-of-the-art model by large margins, with 2.54 f1 and 5.13 f1 improvements on public rams and wikievents datasets respectively, especially on cross-sentence event arguments extraction. ## task formulation following @xcite , we formulate doclevel event argument extraction as follows. we define that a document d consists of n sentences, and a sentence is comprised of a sequence of words, i.e., @xmath0, w 2 , . . . , w |d| , and sen (w i ) ∈ [1, n ] refers to the sentence that w i belongs to. we also define the event types set e and the corresponding argument roles set r e for each event type @xmath1. then, given a document d and the trigger @xmath2, the task aims to detect all (r, s) pairs for the event, where @xmath3, and s ⊆ d is a contiguous text span in the document. ## methodology figure 2 shows the overall architecture of our model tsar. the document is fed into the twostream encoding module, followed by the amrguided interaction module to derive both global and local contextualized representations. the information fusion module fuses these two-stream representations, and the classification module finally predicts argument roles for candidate spans. ## analysis 6.1 cross-sentence argument extraction since there are multiple sentences in the document, some event arguments are located far away from the trigger, which highly increases the difficulty of 2 we use tsar large based on roberta large to compare with bart-gen based on bart large , as they are pre-trained on the same corpus with the same batch size and training steps. ## conclusion it is challenging to extract event arguments from a whole document, owing to the long-distance dependency between trigger and arguments over sentences and the distracting context. to tackle these problems, we propose two-stream amrenhanced extraction model (tsar). tsar uses two-stream encoders to encode the document from different perspectives, followed by an amr-guided interaction module to facilitate the document-level semantic interactions. an auxiliary boundary loss is introduced to enhance the boundary information for spans. experiments on rams and wikievents datasets demonstrate that tsar outperform previous state-of-the-art methods by a large margin, with 2.51 and 5.13 f1 improvements respectively, especially for cross-sentence argument extraction.