Event Coreference Resolution with their Paraphrases and Argument-aware Embeddings (2020)

Abstract: Event coreference resolution aims to classify all event mentions that refer to the same real-world event into the same group, which is necessary to information aggregation and many downstream applications. To resolve event coreference, existing methods usually calculate the similarities between event mentions and between specific kinds of event arguments. However, they fail to accurately identify paraphrase relations between events and may suffer from error propagation while extracting event components (i.e., event mentions and their arguments). Therefore, we propose a new model based on Event-specific Paraphrases and Argument-aware Semantic Embeddings, thus called EPASE, for event coreference resolution. EPASE recognizes deep paraphrase relations in an event-specific context of sentences and can cover event paraphrases of more situations, bringing about a better generalization. Additionally, the embeddings of argument roles are encoded into event embedding without relying on a fixed number and type of arguments, which results in the better scalability of EPASE. Experiments on both within- and cross-document event coreference demonstrate its consistent and significant superiority compared to existing methods.

## introduction event coreference resolution clusters event mentions referring to the same real-world event, no matter within a single document (denoted as wd) or across multiple documents (denoted as cd). it is vital for information aggregation and can further benefit many downstream natural language processing applications, including contradiction detection @xcite ), text mining @xcite and question answering @xcite . usually, each event consists of an event mention and a few arguments. take the following two sentences as an example, where event mentions are marked in bold, and the subscripts indicates their ids: each set constitutes an event coreference chain and the event mentions in it are all coreferential. an intuitive and effective way to resolve event coreference is to calculate the similarities between event mentions and between their arguments. many methods @xcite @xcite have adopted these similarities as important features to train classifiers. essentially, this means is to identify the paraphrase relation between events (i.e., one event can be viewed as a paraphrase of another event) by modeling the similarity between event components (i.e., event mentions and their arguments). however, these methods use these event components separately and ignore deep event paraphrase relations within events' context. in addition, when incorporating event arguments to resolve event coreference, previous methods @xcite @xcite first extract semantic roles through semantic role labeling (srl), match them with entity annotations in the corpus to obtain simplified arguments, and finally fill argument slots to get the specified types of arguments. due to the inconsistency between the output of the srl system and the entity annotations, they often suffer from error propagation. for instance, in the output of the srl system, the arg0 (agent) of confirmed (m3) is the original trainwreck tara reid's publicist, but there are two annotated entities tara reid and publicist. it's hard to distinguish which is the right arg0 using simple matching rules. besides, a fixed number and type of arguments will also cause poor scalability. to address these limitations, we propose an event-specific paraphrases and argument-aware semantic embeddings enhanced model (epase) for within-and cross-document event coreference resolution. here, the event-specific paraphrase, we mean a special kind of paraphrase relation between sentences, only focuses on certain events. for example, when focusing on events told (m 2 ) and confirmed (m 3 ), the above two sentences are paraphrase pairs, but they are not when looking at them from the perspectives of events checked into (m1) and confirmed (m 3 ). in epase, a paraphrase identification module and a special token [coref] are employed to help the model capture the event-specific paraphrase relations. at the same time, we obtain the argument-aware semantic embeddings of events by incorporating the embeddings of semantic role labels with the token embeddings and aggregating these information through a semantic attention mechanism. finally, we integrate these two aspects to get the coreference score between events. in general, the contributions of this paper can be summarized as follows: ## related works according to the development period and adopted methods, we roughly divide the previous works on event coreference resolution into three categories. ## the epase model given a document collection d, where each document d i consists of a series of sentences {s 0 , s 1 , ...s n }. in each sentence, there may be zero or more events. the event coreference resolution task is to find all the events that refer to the same real-world event with the given event in the document set. in our epase model, we formulate it as a pairwise similarity identification task. the overall architecture of our model is shown in figure 1 . we first conduct document clustering to narrow the search scope and construct pairwise samples for model training by sampling. then we employ the event paraphrase identification module to model the event-specific paraphrase similarity vector s para and the semantic similarity evaluation module to model the semantic similarity vector s sem between events. the final similarity vector s is calculated by s sem and s para . ## conclusions and future work we proposed epase to resolve event coreference, which integrates event-specific paraphrases and argument-aware semantic embeddings. compared with obtaining event embeddings within single sentences and calculating event similarity based on them, epase identifies event-specific paraphrases between sentences to capture the correlation between two events, which is more in line with human cognition. meanwhile, to make up for the situation that not all the event arguments can be recognized in the paraphrase identification, we further introduce the embeddings of argument roles to obtain argumentaware semantic embedding of events. thus, epase is of high generalization ability and scalability. experimental results manifest the remarkable superiority of epase. in the future, we will strengthen our srl component to handle event mentions which are pronouns, nouns, and adjectives. also, we will further reduce the reliance on the event mention annotations and construct an end-to-end event coreference resolution framework.