What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Programming language
- Natural language processing
His primary areas of investigation include Artificial intelligence, Machine translation, Natural language processing, Translation and Speech recognition.
His Word, Phrase and Training set study, which is part of a larger body of work in Artificial intelligence, is frequently linked to Evaluation methods, bridging the gap between disciplines.
His Machine translation study incorporates themes from Sentence, Speech corpus, Machine learning and Domain.
In his study, which falls under the umbrella issue of Natural language processing, Conditional random field is strongly linked to Segmentation.
His Translation study combines topics from a wide range of disciplines, such as Language model, Label propagation, Cluster analysis and Interpolation.
The study incorporates disciplines such as Computer-assisted translation, Semantic similarity and Speech synthesis in addition to Example-based machine translation.
His most cited work include:
- Toward a Broad-coverage Bilingual Corpus for Speech Translation of Travel Conversations in the Real World (225 citations)
- Overview of the patent machine translation task at the NTCIR-9 workshop (157 citations)
- EXPERIMENTS AND PROSPECTS OF EXAMPLE-BASED MACHINE TRANSLATION (146 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Natural language processing, Machine translation, Speech recognition and Translation are his primary areas of study.
His Artificial intelligence study frequently links to related topics such as Machine learning.
Natural language processing is represented through his Example-based machine translation, Transfer-based machine translation, Speech translation, Language model and Synchronous context-free grammar research.
The concepts of his Machine translation study are interwoven with issues in Domain and Transformer.
His Speech recognition research includes themes of Transliteration and Segmentation, Text segmentation.
The various areas that Eiichiro Sumita examines in his Translation study include Artificial neural network and Decoding methods.
He most often published in these fields:
- Artificial intelligence (79.08%)
- Natural language processing (69.64%)
- Machine translation (63.27%)
What were the highlights of his more recent work (between 2018-2021)?
- Machine translation (63.27%)
- Artificial intelligence (79.08%)
- Natural language processing (69.64%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Machine translation, Artificial intelligence, Natural language processing, Translation and Transformer.
He has included themes like Artificial neural network, Language model, Speech recognition and Word in his Machine translation study.
His work carried out in the field of Artificial intelligence brings together such families of science as NIST and Machine learning.
His Natural language processing study integrates concerns from other disciplines, such as Agreement, Supervised learning, Rank and Burmese.
His studies examine the connections between Translation and genetics, as well as such issues in German, with regards to Test set.
He works mostly in the field of Transformer, limiting it down to concerns involving Embedding and, occasionally, Pattern recognition.
Between 2018 and 2021, his most popular works were:
- Unsupervised Bilingual Word Embedding Agreement for Unsupervised Neural Machine Translation. (25 citations)
- Data-dependent Gaussian Prior Objective for Language Generation (23 citations)
- NICT’s Unsupervised Neural and Statistical Machine Translation Systems for the WMT19 News Translation Task (19 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Programming language
- Natural language processing
His primary scientific interests are in Machine translation, Artificial intelligence, Natural language processing, Translation and Sentence.
His Machine translation research incorporates elements of Transformer, Word embedding, NIST, Speech recognition and Agreement.
His work on Language model as part of his general Speech recognition study is frequently connected to Structure, thereby bridging the divide between different branches of science.
His study ties his expertise on Machine learning together with the subject of Artificial intelligence.
In general Natural language processing study, his work on Parsing often relates to the realm of Languages of Asia, thereby connecting several areas of interest.
While the research belongs to areas of Sentence, Eiichiro Sumita spends his time largely on the problem of Representation, intersecting his research to questions surrounding Lookup table and Image.
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