What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Natural language processing
Jingjing Liu mainly investigates Artificial intelligence, Commonsense reasoning, Language model, Question answering and Human–computer interaction.
The study incorporates disciplines such as Identification and Natural language processing in addition to Artificial intelligence.
His study looks at the relationship between Natural language processing and topics such as Representation, which overlap with Image.
His work in Commonsense reasoning addresses issues such as Embedding, which are connected to fields such as Feature learning.
Within one scientific family, he focuses on topics pertaining to Machine learning under Language model, and may sometimes address concerns connected to Natural language understanding.
His Question answering study combines topics in areas such as Probabilistic logic and Natural language.
His most cited work include:
- Low-Quality Product Review Detection in Opinion Summarization (315 citations)
- UNITER: Learning UNiversal Image-TExt Representations (156 citations)
- Video search re-ranking via multi-graph propagation (141 citations)
What are the main themes of his work throughout his whole career to date?
Jingjing Liu focuses on Artificial intelligence, Natural language processing, Language model, Question answering and Machine learning.
He frequently studies issues relating to Pattern recognition and Artificial intelligence.
In the subject of general Natural language processing, his work in Automatic summarization, Sentence and Parsing is often linked to Consistency, thereby combining diverse domains of study.
As part of one scientific family, Jingjing Liu deals mainly with the area of Language model, narrowing it down to issues related to the Machine translation, and often Text generation.
His research integrates issues of Matching, Theoretical computer science and Closed captioning in his study of Question answering.
The concepts of his Machine learning study are interwoven with issues in Comprehension and Robustness.
He most often published in these fields:
- Artificial intelligence (65.91%)
- Natural language processing (30.30%)
- Language model (25.00%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (65.91%)
- Question answering (21.97%)
- Language model (25.00%)
In recent papers he was focusing on the following fields of study:
Jingjing Liu spends much of his time researching Artificial intelligence, Question answering, Language model, Transformer and Natural language processing.
In his work, Robustness is strongly intertwined with Machine learning, which is a subfield of Artificial intelligence.
His Question answering research is multidisciplinary, incorporating elements of Sentence, Theoretical computer science, Feature learning and Closed captioning.
The various areas that Jingjing Liu examines in his Transformer study include Information integration, Cluster analysis and Pattern recognition.
His Natural language processing research integrates issues from Coreference and Benchmark.
His study in Commonsense reasoning is interdisciplinary in nature, drawing from both Range and Natural language.
Between 2019 and 2021, his most popular works were:
- FreeLB: Enhanced Adversarial Training for Natural Language Understanding (74 citations)
- UNITER: UNiversal Image-TExt Representation Learning (69 citations)
- DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation (63 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Natural language processing
Jingjing Liu mostly deals with Artificial intelligence, Question answering, Language model, Natural language processing and Transformer.
His work on Commonsense reasoning is typically connected to Quality as part of general Artificial intelligence study, connecting several disciplines of science.
His study looks at the intersection of Question answering and topics like Feature learning with Benchmark.
As a member of one scientific family, Jingjing Liu mostly works in the field of Language model, focusing on Inference and, on occasion, Task analysis, Visualization and Natural language.
His research investigates the connection between Natural language processing and topics such as Coreference that intersect with issues in Margin and Sentence.
His studies deal with areas such as Response generation, Intelligent decision support system, Generative grammar and Human–computer interaction as well as Transformer.
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