no code implementations • 10 Dec 2017 • Yonatan Bisk, Kevin J. Shih, Yejin Choi, Daniel Marcu
In this paper, we study the problem of mapping natural language instructions to complex spatial actions in a 3D blocks world.
no code implementations • WS 2017 • Sudha Rao, Daniel Marcu, Kevin Knight, Hal Daum{\'e} III
We propose a novel, Abstract Meaning Representation (AMR) based approach to identifying molecular events/interactions in biomedical text.
2 code implementations • WS 2016 • Ke Tran, Yonatan Bisk, Ashish Vaswani, Daniel Marcu, Kevin Knight
In this work, we present the first results for neuralizing an Unsupervised Hidden Markov Model.
no code implementations • LREC 2016 • Eunsol Choi, Matic Horvat, Jonathan May, Kevin Knight, Daniel Marcu
Understanding the experimental results of a scientific paper is crucial to understanding its contribution and to comparing it with related work.
1 code implementation • 4 Dec 2015 • Sahil Garg, Aram Galstyan, Ulf Hermjakob, Daniel Marcu
We advance the state of the art in biomolecular interaction extraction with three contributions: (i) We show that deep, Abstract Meaning Representations (AMR) significantly improve the accuracy of a biomolecular interaction extraction system when compared to a baseline that relies solely on surface- and syntax-based features; (ii) In contrast with previous approaches that infer relations on a sentence-by-sentence basis, we expand our framework to enable consistent predictions over sets of sentences (documents); (iii) We further modify and expand a graph kernel learning framework to enable concurrent exploitation of automatically induced AMR (semantic) and dependency structure (syntactic) representations.
no code implementations • 24 Apr 2015 • Michael Pust, Ulf Hermjakob, Kevin Knight, Daniel Marcu, Jonathan May
To make this work, we transform the AMR structure into a form suitable for the mechanics of SBMT and useful for modeling.
no code implementations • 4 Jul 2009 • Hal Daumé III, John Langford, Daniel Marcu
We present Searn, an algorithm for integrating search and learning to solve complex structured prediction problems such as those that occur in natural language, speech, computational biology, and vision.
no code implementations • HLT-NAACL 2003 • Philipp Koehn, Franz J. Och, Daniel Marcu
We propose a new phrase-based translation model and decoding algorithm that enables us to evaluate and compare several, previously proposed phrase-based translation models.