Implicit Argument Prediction with Event Knowledge

Proceedings of NAACL-HLT 2018, pages 831–840 New Orleans, Louisiana, June 1 - 6, 2018. c⃝2018 Association for Computational Linguistics Implicit Argument Prediction with Event Knowledge Pengxiang Cheng Department of Computer Science The University of Texas at Austin pxcheng@cs.utexas.edu Katrin Erk Department of Linguistics The University of Texas at Austin katrin.erk@mail.utexas.edu Abstract Implicit arguments are not syntactically con- nected to their predicates, and are therefore hard to extract. Previous work has used mod- els with large numbers of features, evaluated on very small datasets. We propose to train models for implicit argument prediction on a simple cloze task, for which data can be gen- erated automatically at scale. This allows us to use a neural model, which draws on narrative coherence and entity salience for predictions. We show that our model has superior perfor- mance on both synthetic and natural data. 1 1 Introduction When parts of an event description in a text are missing, this event cannot be easily extracted, and it cannot easily be found as the answer to a ques- tion. This is the case with implicit arguments, as in this example from the reading comprehension dataset of Hermann et al. (2015): Text: More than 2,600 people have been infected by Ebola in Liberia, Guinea, Sierra Leone and Nigeria since the out- break began in December, according to the World Health Organization. Nearly 1,500 have died. Question: The X outbreak has killed nearly 1,500. In this example, it is Ebola that broke out, and Ebola was also the cause of nearly 1,500 people dying, but the text does not state this explicitly. Ebola is an implicit argument of both outbreak and die, which is crucial to answering the question. We are particularly interested in implicit argu- ments that, like Ebola in this case, do appear in the text, but not as syntactic arguments of their 1Our code is available at https://github.com/ pxch/event_imp_arg. predicates. Event knowledge is key to determin- ing implicit arguments. In our example, diseases are maybe the single most typical things to break out, and diseases also typically kill people. The task of identifying implicit arguments was first addressed by Gerber and Chai (2010) and Ruppenhofer et al. (2010). However, the datasets for the task were very small, and to our knowledge there has been very little further development on the task since then. In this paper, we address the data issue by train- ing models for implicit argument prediction on a simple cloze task, similar to the narrative cloze task (Chambers and Jurafsky, 2008), for which data can be generated automatically at scale. This allows us to train a neural network to perform the task, building on two insights. First, event knowl- edge is crucial for implicit argument detection. Therefore we build on models for narrative event prediction (Granroth-Wilding and Clark, 2016; Pi- chotta and Mooney, 2016a), using them to judge how coherent the narrative would be when we fill in a particular entity as the missing (implicit) ar- gument. Second, the omitted arguments tend …

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