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Research - Papers

Explore a selection of our published work on a variety of key research challenges in AI.

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Cross-Sentence Inference for Process Knowledge

Samuel LouvanChetan NaikSadhana Kumaraveland Peter Clark
2016
EMNLP

For AI systems to reason about real world situations, they need to recognize which processes are at play and which entities play key roles in them. Our goal is to extract this kind of rolebased… 

Semantic Parsing to Probabilistic Programs for Situated Question Answering

Jayant KrishnamurthyOyvind Tafjordand Aniruddha Kembhavi
2016
EMNLP

Situated question answering is the problem of answering questions about an environment such as an image or diagram. This problem requires jointly interpreting a question and an environment using… 

Solving Geometry Problems: Combining Text and Diagram Interpretation

Minjoon SeoHannaneh HajishirziAli Farhadiand Clint Malcolm
2015
EMNLP

This paper introduces GeoS, the first automated system to solve unaltered SAT geometry questions by combining text understanding and diagram interpretation. We model the problem of understanding… 

Answering Elementary Science Questions by Constructing Coherent Scenes using Background Knowledge

Yang Li and Peter Clark
2015
EMNLP

Much of what we understand from text is not explicitly stated. Rather, the reader uses his/her knowledge to fill in gaps and create a coherent, mental picture or “scene” depicting what text appears… 

Exploring Markov Logic Networks for Question Answering

Tushar KhotNiranjan BalasubramanianEric Gribkoffand Oren Etzioni
2015
EMNLP

Elementary-level science exams pose significant knowledge acquisition and reasoning challenges for automatic question answering. We develop a system that reasons with knowledge derived from… 

Learning to Solve Arithmetic Word Problems with Verb Categorization

Mohammad Javad HosseiniHannaneh HajishirziOren Etzioniand Nate Kushman
2014
EMNLP

This paper presents a novel approach to learning to solve simple arithmetic word problems. Our system, ARIS, analyzes each of the sentences in the problem statement to identify the relevant… 

Modeling Biological Processes for Reading Comprehension

Jonathan BerantVivek SrikumarPei-Chun Chenand Peter Clark
2014
EMNLP

Machine reading calls for programs that read and understand text, but most current work only attempts to extract facts from redundant web-scale corpora. In this paper, we focus on a new reading… 

Learning Biological Processes with Global Constraints

Aju Thalappillil ScariaJonathan BerantMengqiu Wangand Peter Clark
2013
EMNLP

Biological processes are complex phenomena involving a series of events that are related to one another through various relationships. Systems that can understand and reason over biological… 

Semi-Markov Phrase-based Monolingual Alignment

Xuchen YaoBenjamin Van DurmeChris Callision-Burchand Peter Clark
2013
EMNLP

We introduce a novel discriminative model for phrase-based monolingual alignment using a semi-Markov CRF. Our model achieves stateof-the-art alignment accuracy on two phrasebased alignment datasets… 

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