Her primary areas of study are Artificial intelligence, Natural language processing, Parsing, Question answering and Inference. Her Pattern recognition research extends to Artificial intelligence, which is thematically connected. Her work deals with themes such as Speech recognition and Word, which intersect with Natural language processing.
Her Parsing research integrates issues from Semantic role labeling and Natural language. Her Question answering study combines topics in areas such as Natural language understanding and SemEval. The various areas that Kristina Toutanova examines in her Inference study include Winograd Schema Challenge and Sequence labeling.
Kristina Toutanova focuses on Artificial intelligence, Natural language processing, Machine translation, Parsing and Speech recognition. Her Artificial intelligence study incorporates themes from Machine learning and Pattern recognition. In her study, which falls under the umbrella issue of Natural language processing, Dependency is strongly linked to Set.
As a part of the same scientific family, she mostly works in the field of Machine translation, focusing on Syntax and, on occasion, BLEU. Kristina Toutanova combines subjects such as Computational linguistics, Head-driven phrase structure grammar, Grammar, Selection and Natural language with her study of Parsing. Her Language model research is multidisciplinary, relying on both Inference and Reading comprehension.
Artificial intelligence, Question answering, Natural language processing, Information retrieval and String are her primary areas of study. Artificial intelligence is closely attributed to Machine learning in her work. Kristina Toutanova focuses mostly in the field of Question answering, narrowing it down to topics relating to Context and, in certain cases, Paragraph, Sentiment analysis, Sentence and Automatic summarization.
Her research in Natural language processing is mostly focused on Language model. In Language model, Kristina Toutanova works on issues like Reading comprehension, which are connected to Paraphrase, Transfer of learning, Logical consequence and Inference. The Ranking research Kristina Toutanova does as part of her general Information retrieval study is frequently linked to other disciplines of science, such as Natural, Sequence and Autoregressive model, therefore creating a link between diverse domains of science.
Kristina Toutanova spends much of her time researching Artificial intelligence, Information retrieval, Question answering, Natural language processing and Machine learning. In the subject of general Artificial intelligence, her work in Paraphrase, Logical consequence and Inference is often linked to Term and Meaning, thereby combining diverse domains of study. Her study on Open domain is often connected to Natural as part of broader study in Information retrieval.
The study incorporates disciplines such as Search engine and Data set in addition to Question answering. The Natural language processing study combines topics in areas such as Domain, Metadata, Reading, Construct and Entity linking. Her research links Natural language with Machine learning.
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Jacob Devlin;Ming-Wei Chang;Kenton Lee;Kristina N. Toutanova.
north american chapter of the association for computational linguistics (2018)
Feature-rich part-of-speech tagging with a cyclic dependency network
Kristina Toutanova;Dan Klein;Christopher D. Manning;Yoram Singer.
north american chapter of the association for computational linguistics (2003)
Natural Questions: A Benchmark for Question Answering Research
Tom Kwiatkowski;Jennimaria Palomaki;Olivia Redfield;Michael Collins.
Transactions of the Association for Computational Linguistics (2019)
Latent Retrieval for Weakly Supervised Open Domain Question Answering
Kenton Lee;Ming-Wei Chang;Kristina N. Toutanova.
meeting of the association for computational linguistics (2019)
Representing Text for Joint Embedding of Text and Knowledge Bases
Kristina Toutanova;Danqi Chen;Patrick Pantel;Hoifung Poon.
empirical methods in natural language processing (2015)
Observed versus latent features for knowledge base and text inference
Kristina Toutanova;Danqi Chen.
Proceedings of the 3rd Workshop on Continuous Vector Space Models and their Compositionality (2015)
Cross-Sentence N-ary Relation Extraction with Graph LSTMs
Nanyun Peng;Hoifung Poon;Chris Quirk;Kristina Toutanova.
Transactions of the Association for Computational Linguistics (2017)
BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions
Christopher Clark;Kenton Lee;Ming-Wei Chang;Tom Kwiatkowski.
north american chapter of the association for computational linguistics (2019)
Pronunciation Modeling for Improved Spelling Correction
Kristina Toutanova;Robert Moore.
meeting of the association for computational linguistics (2002)
Extracting Parallel Sentences from Comparable Corpora using Document Level Alignment
Jason R. Smith;Chris Quirk;Kristina Toutanova.
north american chapter of the association for computational linguistics (2010)
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