We propose a new framework for supervised machine learning. Our goal is to learn from smaller amounts of supervised training data, by collecting a richer kind of training data: an...
To enable conversational QA, it is important to examine key issues addressed in conversational systems in the context of question answering. In conversational systems, understandi...
We present an automatic approach to tree annotation in which basic nonterminal symbols are alternately split and merged to maximize the likelihood of a training treebank. Starting...
Slav Petrov, Leon Barrett, Romain Thibaux, Dan Kle...
We first show how a structural locality bias can improve the accuracy of state-of-the-art dependency grammar induction models trained by EM from unannotated examples (Klein and Ma...
This paper presents the particular use of "Jibiki" (Papillon's web server development platform) for the LexALP1 project. LexALP's goal is to harmonise the term...