Heather Lent


2019

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Rewarding Coreference Resolvers for Being Consistent with World Knowledge
Rahul Aralikatte | Heather Lent | Ana Valeria Gonzalez | Daniel Herschcovich | Chen Qiu | Anders Sandholm | Michael Ringaard | Anders Søgaard
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Unresolved coreference is a bottleneck for relation extraction, and high-quality coreference resolvers may produce an output that makes it a lot easier to extract knowledge triples. We show how to improve coreference resolvers by forwarding their input to a relation extraction system and reward the resolvers for producing triples that are found in knowledge bases. Since relation extraction systems can rely on different forms of supervision and be biased in different ways, we obtain the best performance, improving over the state of the art, using multi-task reinforcement learning.

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Eidos, INDRA, & Delphi: From Free Text to Executable Causal Models
Rebecca Sharp | Adarsh Pyarelal | Benjamin Gyori | Keith Alcock | Egoitz Laparra | Marco A. Valenzuela-Escárcega | Ajay Nagesh | Vikas Yadav | John Bachman | Zheng Tang | Heather Lent | Fan Luo | Mithun Paul | Steven Bethard | Kobus Barnard | Clayton Morrison | Mihai Surdeanu
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations)

Building causal models of complicated phenomena such as food insecurity is currently a slow and labor-intensive manual process. In this paper, we introduce an approach that builds executable probabilistic models from raw, free text. The proposed approach is implemented through three systems: Eidos, INDRA, and Delphi. Eidos is an open-domain machine reading system designed to extract causal relations from natural language. It is rule-based, allowing for rapid domain transfer, customizability, and interpretability. INDRA aggregates multiple sources of causal information and performs assembly to create a coherent knowledge base and assess its reliability. This assembled knowledge serves as the starting point for modeling. Delphi is a modeling framework that assembles quantified causal fragments and their contexts into executable probabilistic models that respect the semantics of the original text, and can be used to support decision making.