Artificial intelligence, Natural language processing, Parsing, Natural language and Question answering are his primary areas of study. Language model, Probabilistic logic, Logical form, Semantics and Deep learning are the primary areas of interest in his Artificial intelligence study. His Natural language processing research incorporates elements of Context, Meaning and Reading comprehension.
His Parsing study combines topics from a wide range of disciplines, such as Lexical item, Lift, Rule-based machine translation and Benchmark. Luke Zettlemoyer interconnects Machine learning and Knowledge base in the investigation of issues within Natural language. He combines subjects such as Scheme and Ontology alignment with his study of Question answering.
His primary areas of study are Artificial intelligence, Natural language processing, Parsing, Language model and Natural language. The Artificial intelligence study combines topics in areas such as Context and Machine learning. His Machine learning research focuses on subjects like Question answering, which are linked to Scheme.
The study incorporates disciplines such as Semantics and Meaning in addition to Natural language processing. His research in Language model tackles topics such as Machine translation which are related to areas like Decoding methods and Transformer. His Natural language research integrates issues from Robot and Human–computer interaction.
The scientist’s investigation covers issues in Artificial intelligence, Natural language processing, Machine translation, Language model and Benchmark. In his study, Sentence is inextricably linked to Machine learning, which falls within the broad field of Artificial intelligence. Within one scientific family, Luke Zettlemoyer focuses on topics pertaining to Textual entailment under Sentence, and may sometimes address concerns connected to Question answering.
Luke Zettlemoyer has researched Natural language processing in several fields, including Embedding, Word embedding, Word-sense disambiguation and Protocol. His Machine translation research is multidisciplinary, incorporating elements of Speech recognition and Decoding methods. His research integrates issues of Algorithm, Cross lingual, Automatic summarization and Pattern recognition in his study of Language model.
Luke Zettlemoyer mostly deals with Artificial intelligence, Speech recognition, Language model, Machine translation and Natural language processing. His study in Rewriting extends to Artificial intelligence with its themes. His research investigates the connection between Speech recognition and topics such as BLEU that intersect with problems in Automatic summarization.
His studies in Language model integrate themes in fields like Urdu, Word, Swahili and Similarity. Many of his research projects under Natural language processing are closely connected to Supervised learning with Supervised learning, tying the diverse disciplines of science together. His biological study spans a wide range of topics, including Machine learning, Representation and Noise.
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RoBERTa: A Robustly Optimized BERT Pretraining Approach
Yinhan Liu;Myle Ott;Naman Goyal;Jingfei Du.
arXiv: Computation and Language (2019)
Deep contextualized word representations
Matthew E. Peters;Mark Neumann;Mohit Iyyer;Matt Gardner.
north american chapter of the association for computational linguistics (2018)
Unsupervised Cross-lingual Representation Learning at Scale
Alexis Conneau;Kartikay Khandelwal;Naman Goyal;Vishrav Chaudhary.
meeting of the association for computational linguistics (2020)
Learning to map sentences to logical form: structured classification with probabilistic categorial grammars
Luke S. Zettlemoyer;Michael Collins.
uncertainty in artificial intelligence (2005)
BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension.
Mike Lewis;Yinhan Liu;Naman Goyal;Marjan Ghazvininejad.
arXiv: Computation and Language (2019)
AllenNLP: A Deep Semantic Natural Language Processing Platform
Matt Gardner;Joel Grus;Mark Neumann;Oyvind Tafjord.
Proceedings of Workshop for NLP Open Source Software (NLP-OSS) (2018)
Knowledge-Based Weak Supervision for Information Extraction of Overlapping Relations
Raphael Hoffmann;Congle Zhang;Xiao Ling;Luke Zettlemoyer.
meeting of the association for computational linguistics (2011)
TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension
Mandar Joshi;Eunsol Choi;Daniel S. Weld;Luke Zettlemoyer.
meeting of the association for computational linguistics (2017)
SpanBERT: Improving Pre-training by Representing and Predicting Spans
Mandar Joshi;Danqi Chen;Yinhan Liu;Daniel S. Weld.
Transactions of the Association for Computational Linguistics (2020)
End-to-end Neural Coreference Resolution
Kenton Lee;Luheng He;Mike Lewis;Luke Zettlemoyer.
empirical methods in natural language processing (2017)
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