Capturing Event Argument Interaction via A Bi-Directional Entity-Level Recurrent Decoder (2021)

Abstract: Capturing interactions among event arguments is an essential step towards robust event argument extraction (EAE). However, existing efforts in this direction suffer from two limitations: 1) The argument role type information of contextual entities is mainly utilized as training signals, ignoring the potential merits of directly adopting it as semantically rich input features; 2) The argument-level sequential semantics, which implies the overall distribution pattern of argument roles over an event mention, is not well characterized. To tackle the above two bottlenecks, we formalize EAE as a Seq2Seq-like learning problem for the first time, where a sentence with a specific event trigger is mapped to a sequence of event argument roles. A neural architecture with a novel Bi-directional Entity-level Recurrent Decoder (BERD) is proposed to generate argument roles by incorporating contextual entities’ argument role predictions, like a word-by-word text generation process, thereby distinguishing implicit argument distribution patterns within an event more accurately.

## introduction event argument extraction (eae), which aims to identify the entities serving as event arguments and classify the roles they play in an event, is a key step towards event extraction (ee). for example, given that the word "fired" triggers an attack event in the sentence "in baghdad, a cameraman died when an american tank fired on the palestine hotel" , eae need to identify that "baghdad", "cameraman", "american tank", and "palestine hotel" are arguments with place, target, instrument, and target as roles respectively. recently, deep learning models have been widely applied to event argument extraction and † corresponding authors. achieved significant progress @xcite @xcite @xcite @xcite . many efforts have been devoted to improving eae by better characterizing argument interaction, categorized into two paradigms. the first one, named inter-event argument interaction in this paper, concentrates on mining information of the target entity (candidate argument) in the context of other event instances @xcite , e.g., the evidence that a victim argument for the die event is often the target argument for the attack event in the same sentence. the second one is intra-event argument interaction, which exploits the relationship of the target entity with others in the same event instance @xcite @xcite . we focus on the second paradigm in this paper. despite their promising results, existing methods on capturing intra-event argument interaction suffer from two bottlenecks. to address the above two challenges, we formalize eae as a seq2seq-like learning problem @xcite of mapping a sentence with a specific event trigger to a sequence of event argument roles. to fully utilize both left-and rightside argument role information, inspired by the bidirectional decoder for machine translation @xcite , we propose a neural architecture with a novel bi-directional entity-level recurrent decoder (berd) to generate event argument roles entity by entity. the predicted argument role of an entity is fed into the decoding module for the next 1 in the experiment we skip the event detection phase and directly assume all the triggers are correctly recognized. or previous entity recurrently like a text generation process. in this way, berd can identify candidate arguments in a way that is more consistent with the implicit distribution pattern of multiple argument roles within a sentence, similar to text generation models that learn to generate word sequences following certain grammatical rules or text styles. the contributions of this paper are: ## problem formulation most previous works formalize eae as either a word-level sequence labeling problem @xcite @xcite or an entity-oriented classic classification problem @xcite . we formalize eae as a seq2seq-like learning problem as follows. let @xmath0, ..., w n } be a sentence where n is the sentence length and w i is the i-th token. also, let @xmath1, ..., e k } be the entity mentions in the sentence where k is number of entities. given that an event triggered by @xmath2, eae need to map the sentence with the event to a sequence of argument roles @xmath3, ..., y k }, where y i denotes the argument role that entity e i plays in the event. ## the proposed approach we employ an encoder-decoder architecture for the problem defined above, which is similar to most seq2seq models in machine translation @xcite , automatic text summarization @xcite , and speech recognition @xcite from a high-level perspective. be d ed e a a e ca a ed a ca e a a w dp ece se e + p + e c d e pa e e h e # attack # [sep] 1 a g e fea e e ac start i a ce fea e e ac cla i e 2 a g e fea e e ac i a ce fea e e ac cla i e u da e 3 a g e fea e e ac i a ce fea e e ac cla i e 4 a g e fea e e ac i a ce fea e e ac cla i e i a ce fea e e ac a g e fea e e ac cla i e start 4 u da e i a ce fea e e ac a g e fea e e ac cla i e 3 u da e i a ce fea e e ac a g e fea e e ac cla i e 2 u da e i a ce fea e e ac a g e fea e e ac cla i e 1 cla i e cla i e cla i e cla i e f a d d c d bac a d d c d u da e u da e 𝒚 𝟏 𝒚 𝟐 𝒚 𝟑 𝒚 𝟒 𝒙 𝟏 𝒂 𝒙 𝟐 𝒂 𝒙 𝟑 𝒂 𝒙 𝟒 𝒂 𝒙 𝟏 𝒂 𝒙 𝟐 𝒂 𝒙 𝟑 𝒂 𝒙 𝟒 𝒂 𝒙 𝟏 𝒙 𝟐 𝒙 𝟑 𝒙 𝟒 𝒚 𝟏 𝒚 𝟐 𝒚 𝟑 𝒚 𝟒 𝒚 𝟏 𝒚 𝟐 𝒚 𝟑 𝒚 𝟒 𝒙 𝟏 𝒂 𝒙 𝟐 𝒂 𝒙 𝟑 𝒂 𝒙 𝟒 𝒂 𝒙 𝟏 𝒙 𝟐 𝒙 𝟑 𝒙 𝟒 𝒙 𝟏 𝒂 𝒙 𝟐 𝒂 𝒙 𝟑 𝒂 𝒙 𝟒 𝒂 𝒙 𝟏 𝒙 𝟐 𝒙 𝟑 𝒙 𝟒 in particular, as figure 1 shows, our architecture consists of an encoder that converts the sentence s with a specific event trigger into intermediate vectorized representation and a decoder that generates a sequence of argument roles entity by entity. the decoder is an entity-level recurrent network whose number of decoding steps is fixed, the same as the entity number in the corresponding sentence. on each decoding step, we feed the prediction results of the previously-processed entity into the recurrent unit to make prediction for the current entity. since the predicted results of both left-and right-side entities can be potentially valuable information,we further incorporate a bidirectional decoding mechanism that integrates a forward decoding process and a backward decoding process effectively. ## related work we have covered research on eae in section 1, related work that inspires our technical design is mainly introduced in the following. though our recurrent decoder is entity-level, our bidirectional decoding mechanism is inspired by some bidirectional decoders in token-level seq2seq models, e.g., of machine translation @xcite , speech recognition @xcite and scene text recognition @xcite . we formalize the task of eae as a seq2seq-like learning problem instead of a classic classification problem or sequence labeling problem. we have found that there are also some works performing classification or sequence labeling in a seq2seq manner in other fields. for example, @xcite formulates the multi-label classification task as a sequence generation problem to capture the correlations between labels. @xcite explores an encoder-decoder model for semantic role labeling. we are the first to employ a seq2seqlike architecture to solve the eae task. ## conclusion we have presented berd, a neural architecture with a bidirectional entity-level recurrent decoder that achieves competitive performance on the task of event argument extraction (eae). one main characteristic that distinguishes our techniques from previous works is that we formalize eae as a seq2seq-like learning problem instead of a classic classification or sequence labeling problem. the novel bidirectional decoding mechanism enables our berd to utilize both the left-and rightside argument predictions effectively to generate a sequence of argument roles that follows overall distribution patterns over a sentence better. as pioneer research that introduces the seq2seqlike architecture into the eae task, berd also faces some open questions. for example, since we use gold argument roles as prediction results during training, how to alleviate the exposure bias problem is worth investigating. we are also interested in incorporating our techniques into more sophisticated models that jointly extract triggers and arguments.