Event Coreference Resolution with Non-Local Information (2020)

Abstract: We present two extensions to a state-of-theart joint model for event coreference resolution, which involve incorporating (1) a supervised topic model for improving trigger detection by providing global context, and (2) a preprocessing module that seeks to improve event coreference by discarding unlikely candidate antecedents of an event mention using discourse contexts computed based on salient entities. The resulting model yields the best results reported to date on the KBP 2017 English and Chinese datasets.

## introduction event coreference resolution is the task of determining the event mentions in a document that refer to the same real-world event. one of its major challenges concerns error propagation: since the event coreference resolution component typically lies towards the end of the standard information extraction pipeline, the performance of an event coreference resolver can be adversely affected by errors propagated from its upstream components. the upstream component that has the largest impact on event coreference performance is arguably trigger detection. recall that the goal of a trigger detector is to identify event triggers and assign an event subtype to each of them. failure to detect triggers could therefore limit the upper bound on event coreference performance. to address error propagation, one way that has been shown to be effective for a variety of nlp tasks is to develop joint models, which allow crosstask output constraints to be learned from annotated training data. for event coreference, a learner can easily learn, for instance, that two coreferent event mentions must have the same event subtype, thereby allowing event coreference to influence trigger detection. unfortunately, the vast majority of existing event coreference resolvers have adopted a pipeline architecture where trigger detection precedes event coreference. in particular, joint models are both under-studied and under-exploited for event coreference given the usefulness they have demonstrated for other nlp tasks. one exception is @xcite joint model, which jointly learns trigger detection and event coreference and has achieved state-of-the-art results. as a structured conditional random field, the model employs unary factors to encode the features specific for each task and binary/ternary factors to capture the interaction between each pair of tasks. the use of binary/ternary factors is a particularly appealing aspect of this model: it allows these cross-task interactions to be captured in a soft manner, enabling the learner to learn which combinations of values of the output variables are more probable. we hypothesize that the power of this joint event coreference model has not been fully exploited and seek to extend it in this paper. our extensions are based on the observation that the strength of a joint model stems from its ability to facilitate cross-task knowledge transfer. in other words, the better we can model each task involved, the more we can potentially get out of joint modeling. given this observation, we seek to improve the modeling of these tasks in this joint model as follows. first, we improve trigger detection by exploiting topic information. state-of-the-art trigger detectors, including those based on deep neural networks (e.g., @xcite ), classify each candidate trigger using local information and largely ignore the fact that the topic of the document in which a trigger appears plays an important role in determining its event subtype. to understand the usefulness three journalists at the new york times on tuesday announced plans to {leave}ev1 the newspaper. the {departures}ev2 follow moves last month by several other times employees, all of whom were {leaving}ev3 to join digital companies. pakistan's interior ministry has ordered new york times reporter to {leave}ev4. the ministry gave no explanation for the expulsion order. "you are therefore advised to {leave}ev5 the country within 72 hours," the order stated. second, we improve event coreference by exploiting discourse information. specifically, we introduce a preprocessing component for event coreference resolution where we prune the candidate antecedents of an event mention that are unlikely to be its correct antecedent based on discourse context. in essence, this discourse-based preprocessing step seeks to simplify the job of the event coreference model by reducing the number of candidate antecedents it has to consider for a given event mention. we encode the discourse context of an event mention using the entities that are salient at the point of the discourse in which the event mention appears. to our knowledge, we are the first to show that event coreference performance can be improved using discourse contexts that are encoded using salient discourse entities. in sum, the contributions of this paper are twofold. first, while existing event coreference resolvers have largely focused on exploiting the information extracted from the local contexts of the event mentions under consideration, we show how a state-of-the-art joint event coreference model can be improved using the non-local information provided by a supervised topic model and salient discourse entities. second, the resulting model achieves the best results to date on the kbp 2017 english and chinese event coreference datasets. ## model following @xcite , we employ a structured conditional random field, which operates at the document level. specifically, given a test document, we first extract from it all single-and multiword nouns and verbs that have appeared at least once as a trigger in the training data. we treat each of these extracted nouns and verbs as a candidate event mention. the goal of the model is to make joint predictions for the candidate event mentions in a document. three predictions will be made for each candidate event mention that correspond to the three tasks in the model: its trigger subtype, its induced topic, and its antecedent. given this formulation, we define three types of output variables. the first type consists of event subtype variables @xmath0, . . . , s n ). each s i takes a value in the set of the 18 event subtypes defined in kbp 2017 or none, which indicates that the event mention is not a trigger. the second type consists of coreference variables @xmath1, . . . , c n ), where c @xmath2, . . . , i -1, new}. in other words, the value of each c i is the id of its antecedent, which can be one of the preceding event mentions, or new (if the mention underlying c i starts a new cluster). the third type consists of topic variables @xmath3, . . . , t n ). each t i takes a value in a 19-element set in which the topics have a one-to-one correspondence with the event subtype labels defined above. despite this one-to-one mapping, these two types of labels should not be interpreted in the same manner. as we will see, a word's induced topic label is influenced by our supervised topic model, whereas a word's subtype is not. each candidate event mention is associated with one coreference variable, one event subtype variable, and one topic variable. our model induces a probability distribution over these variables: ## related work using topics and salience. for event coreference, the notion of "topics" has thus far been exploited only for cross-document event coreference, where documents are clustered by topics so that no cross-document coreference links can be established between documents in different clusters @xcite . these resolvers, unlike ours, are pipelined systems, meaning that topic detection can influence event coreference resolution but not the other way round. as for discourse salience, we are not aware of any event coreference work that attempts to explicitly model it, although one can argue that existing systems may have implicitly encoded it in a shallow manner via exploiting features that encode the distance between two event mentions @xcite . computing argument compatibility. in addition to discourse-based pruning, candidate antecedents can be pruned based on how compatible the arguments of the two event mentions are. to capture argument compatibility, argument features have been extensively exploited. basic features such as the number of overlapping arguments and the number of unique arguments, and a binary feature encoding whether arguments are conflicting have been proposed @xcite @xcite . more sophisticated features based on different kinds of similarity measures have also been considered, such as the surface similarity based on dice coefficient and the wupalmer wordnet similarity between argument heads @xcite @xcite . these features are computed using either the outputs of event argument extractors and entity coreference resolvers @xcite @xcite or the outputs of semantic parsers @xcite @xcite , and therefore suffer from error propagation (see @xcite ). several previous works proposed joint models to address this problem @xcite , while others utilized iterative methods to propagate argument information @xcite in order to alleviate this issue. nevertheless, argument extraction remains a very challenging task, especially when the arguments do not appear in the same sentence as the trigger. our discourse-based pruning method can be thought of as a way of approximating argument compatibility without performing argument extraction. ## conclusion we incorporated non-local information into a stateof-the-art joint model for event coreference resolution via topic modeling and discourse-based pruning. the resulting model not only significantly outperforms the independent models but also achieves the best results to date on the kbp 2017 english and chinese event coreference corpora.