Eric Lehman, Sarthak Jain, Karl Pichotta, Yoav Goldberg, Byron Wallace. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2021.

Scripts encode knowledge of prototypical sequences of events. We describe a Recurrent Neural Network model for statistical script learning using Long Short-Term Memory, an architecture which has been demonstrated to work well on a range of Artificial Intelligence tasks. We evaluate our system on two tasks, inferring held-out events from text and inferring novel events from text, substantially outperforming prior approaches on both tasks.

There is a small but growing body of research on statistical scripts, models of event sequences that allow probabilistic inference of implicit events from documents.These systems operate on structured verb-argument events produced by an NLP pipeline.We compare these systems with recent Recurrent Neural Net models that directly operate on raw tokens to predict sentences, finding the latter to be roughly comparable to the former in terms of predicting missing events in documents.

Scripts represent knowledge of stereotypical event sequences that can aid text understanding.Initial statistical methods have been developed to learn probabilistic scripts from raw text corpora; however, they utilize a very impoverished representation of events, consisting of a verb and one dependent argument.We present a script learning approach that employs events with multiple arguments.Unlike previous work, we model the interactions between multiple entities in a script.Experiments on a large corpus using the task of inferring held-out events (the "narrative cloze evaluation") demonstrate that modeling multi-argument events improves predictive accuracy.

We address the problem of identifying multiword expressions in a language, focusing on English phrasal verbs.Our polyglot ranking approach integrates frequency statistics from translated corpora in 50 different languages.Our experimental evaluation demonstrates that combining statistical evidence from many parallel corpora using a novel ranking-oriented boosting algorithm produces a comprehensive set of English phrasal verbs, achieving performance comparable to a human-curated set. *Research conducted during an internship at Google.

Abstract Generalized relational theories with null values in the sense of Reiter are first-order theories that provide a semantics for relational databases with incomplete information. In this paper we show that any such theory can be turned into an equivalent logic program, so that models of the theory can be generated using computational methods of answer set programming. As a step towards this goal, we develop a general method for calculating stable models under the domain closure assumption but without the unique name assumption.