What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Programming language
- Statistics
His primary scientific interests are in Artificial intelligence, Natural language processing, Information retrieval, Web page and Machine learning.
His research in Artificial intelligence intersects with topics in Isolation, Probability distribution and Cache.
Shuming Shi has researched Natural language processing in several fields, including Similarity and Inference.
His research investigates the connection with Information retrieval and areas like Ranking which intersect with concerns in Vector space model, Human–computer information retrieval, Cognitive models of information retrieval and Image retrieval.
His studies deal with areas such as Class, Corpus based, F1 score and Font as well as Web page.
His work on Artificial neural network as part of general Machine learning research is often related to Word problem, thus linking different fields of science.
His most cited work include:
- Directional Skip-Gram: Explicitly Distinguishing Left and Right Context for Word Embeddings (138 citations)
- Web object retrieval (113 citations)
- Learning to Remember Translation History with a Continuous Cache (100 citations)
What are the main themes of his work throughout his whole career to date?
Shuming Shi mainly investigates Artificial intelligence, Natural language processing, Machine translation, Information retrieval and Translation.
Shuming Shi focuses mostly in the field of Artificial intelligence, narrowing it down to topics relating to Machine learning and, in certain cases, Matching.
His work on Syntax, Language model and Phrase as part of his general Natural language processing study is frequently connected to Structure, thereby bridging the divide between different branches of science.
His research in the fields of BLEU overlaps with other disciplines such as Transformer.
Information retrieval is closely attributed to Web page in his research.
His research integrates issues of NIST and Representation in his study of Translation.
He most often published in these fields:
- Artificial intelligence (46.73%)
- Natural language processing (32.16%)
- Machine translation (22.61%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (46.73%)
- Natural language processing (32.16%)
- Machine translation (22.61%)
In recent papers he was focusing on the following fields of study:
Shuming Shi mostly deals with Artificial intelligence, Natural language processing, Machine translation, Translation and Machine learning.
Shuming Shi undertakes multidisciplinary investigations into Artificial intelligence and Conversation in his work.
His Natural language processing research includes themes of Artificial neural network and Social media.
His Machine translation study combines topics from a wide range of disciplines, such as Theoretical computer science and Inference.
His Translation study combines topics in areas such as NIST, Representation and Reinforcement learning.
The various areas that Shuming Shi examines in his Machine learning study include Matching, Training set and Empirical research.
Between 2018 and 2021, his most popular works were:
- On the Word Alignment from Neural Machine Translation (38 citations)
- Self-Attention with Structural Position Representations. (29 citations)
- Generating Multiple Diverse Responses for Short-Text Conversation (21 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Programming language
His scientific interests lie mostly in Artificial intelligence, Machine translation, Natural language processing, Translation and Machine learning.
His work blends Artificial intelligence and Conversation studies together.
His Machine translation research focuses on subjects like Theoretical computer science, which are linked to Pooling, Rule-based machine translation and Key.
His Sentence and Phrase study, which is part of a larger body of work in Natural language processing, is frequently linked to Space, bridging the gap between disciplines.
The Translation study combines topics in areas such as NIST, Syntax and Word.
His Relevance study in the realm of Machine learning connects with subjects such as Quality.
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