What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Natural language processing
His primary areas of investigation include Artificial intelligence, Machine translation, Translation, Natural language processing and Speech recognition.
His work in Artificial intelligence addresses subjects such as Machine learning, which are connected to disciplines such as Graph.
His work in Machine translation addresses issues such as Relevance, which are connected to fields such as Programming language.
His Translation research is multidisciplinary, relying on both Isolation, Recurrent neural network and Substitution.
His work carried out in the field of Natural language processing brings together such families of science as Earth mover's distance and Word, Word embedding.
His research in Speech recognition focuses on subjects like Sentence, which are connected to Hidden layer, BLEU and Delimiter.
His most cited work include:
- Modeling Coverage for Neural Machine Translation (509 citations)
- Minimum Risk Training for Neural Machine Translation (312 citations)
- Tree-to-String Alignment Template for Statistical Machine Translation (303 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Artificial intelligence, Machine translation, Natural language processing, Translation and Word.
As part of his studies on Artificial intelligence, he often connects relevant areas like Machine learning.
His Machine translation study frequently draws connections between related disciplines such as Speech recognition.
His Natural language processing course of study focuses on Discriminative model and Generative grammar.
His Translation study integrates concerns from other disciplines, such as Decoding methods, Theoretical computer science and Joint.
He focuses mostly in the field of Word, narrowing it down to matters related to Viterbi algorithm and, in some cases, Parallel corpora.
He most often published in these fields:
- Artificial intelligence (80.43%)
- Machine translation (58.70%)
- Natural language processing (54.35%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (80.43%)
- Machine translation (58.70%)
- Natural language processing (54.35%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Artificial intelligence, Machine translation, Natural language processing, Translation and Word.
His study in the fields of Artificial neural network under the domain of Artificial intelligence overlaps with other disciplines such as Process.
His biological study spans a wide range of topics, including Recurrent neural network, Speech recognition, Decoding methods and Transformer.
His Natural language processing study combines topics in areas such as Deep learning and Task analysis.
His Translation study incorporates themes from Context, Isolation and Task.
His Word research includes themes of Feature learning, System combination and Focus.
Between 2017 and 2021, his most popular works were:
- Improving the Transformer Translation Model with Document-Level Context (115 citations)
- Learning to Remember Translation History with a Continuous Cache (100 citations)
- Towards Robust Neural Machine Translation (98 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Natural language processing
Yang Liu mainly investigates Artificial intelligence, Machine translation, Natural language processing, Translation and Speech recognition.
In most of his Artificial intelligence studies, his work intersects topics such as Machine learning.
His Machine translation research integrates issues from Sentence, Zero and Source code.
His Natural language processing study combines topics from a wide range of disciplines, such as Fragment and Deep learning.
His research ties Isolation and Translation together.
His research integrates issues of Decoding methods, BLEU and Robustness in his study of Speech recognition.
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