K
Kristina Toutanova
Researcher at Google
Publications - 121
Citations - 68587
Kristina Toutanova is an academic researcher from Google. The author has contributed to research in topics: Machine translation & Parsing. The author has an hindex of 47, co-authored 113 publications receiving 40174 citations. Previous affiliations of Kristina Toutanova include Microsoft & Stanford University.
Papers
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Journal ArticleDOI
A global joint model for semantic role labeling
Kristina Toutanova,Kristina Toutanova,Kristina Toutanova,Aria Haghighi,Aria Haghighi,Aria Haghighi,Christopher D. Manning,Christopher D. Manning,Christopher D. Manning +8 more
TL;DR: A model for semantic role labeling that effectively captures the linguistic intuition that a semantic argument frame is a joint structure, with strong dependencies among the arguments, and how to incorporate these strong dependencies in a statistical joint model with a rich set of features over multiple argument phrases is presented.
Posted Content
Well-Read Students Learn Better: The Impact of Student Initialization on Knowledge Distillation
TL;DR: It is observed that applying language model pre-training to students unlocks their generalization potential, surprisingly even for very compact networks.
Journal ArticleDOI
LinGO Redwoods: A Rich and Dynamic Treebank for HPSG
TL;DR: The Linguistic Grammars On-Line (LinGo) Redwoods initiative is presented, a seed activity in the design and development of a new type of treebank, rich in nature and dynamic in both the ways linguistic data can be retrieved from the treebank in varying granularity and the constant evolution and regular updating of the tree bank itself.
Posted Content
Latent Retrieval for Weakly Supervised Open Domain Question Answering
TL;DR: It is shown for the first time that it is possible to jointly learn the retriever and reader from question-answer string pairs and without any IR system, and outperforming BM25 by up to 19 points in exact match.
Patent
Semi-supervised part-of-speech tagging
Kristina Toutanova,Mark Johnson +1 more
TL;DR: In this paper, a word is selected from a received text and features are identified from the word. The features are applied to a model to identify probabilities for sets of part-of-speech tags.