Austin Matthews
Impact in
- Artificial Intelligence top 5%
- Topic Modeling
- Natural Language Processing Techniques
- Text Readability and Simplification
- Speech Recognition and Synthesis
- Advanced Text Analysis Techniques
- Sentiment Analysis and Opinion Mining
- Speech and dialogue systems
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- Multimodal Machine Learning Applications
Papers in
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- Natural Language Processing Techniques 7
- Topic Modeling 7
- Text Readability and Simplification 2
- Speech Recognition and Synthesis 2
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- Handwritten Text Recognition Techniques 1
- Multimodal Machine Learning Applications 1
- Co-authors
- Chris DyerLing WangMiguel BallesterosNoah A. SmithGraham NeubigAlon LavieWaleed AmmarXinyi Wang
- Journals
- Workshop on Statistical Machine Translation (1 paper)Monash University Research Portal (Monash University) (1 paper)
- Partner nations
- United StatesGermanySpain
In The Last Decade
Austin Matthews
5 papers receiving 382 citations
Hit Papers
Peers
Comparison fields: 5 of 60
- Artificial Intelligence 355
- Computer Vision and Pattern Recognition 82
- Information Systems 30
- Management Science and Operations Research 11
- Signal Processing 9
Countries citing papers authored by Austin Matthews
This map shows the geographic impact of Austin Matthews's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Austin Matthews with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Austin Matthews more than expected).
Fields of papers citing papers by Austin Matthews
This network shows the impact of papers produced by Austin Matthews. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Austin Matthews. The network helps show where Austin Matthews may publish in the future.
Co-authors
The 22 scholars most cited alongside Austin Matthews, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2019 | 0 | |
| 2 | XNMT: the eXtensible Neural Machine Translation toolkit | 2018 | 13 |
| 3 | 2018 | 10 | |
| 4 | 2016 | 1 | |
| 5 | Transition-Based Dependency Parsing with Stack Long Short-Term Memory Hit paper breakdown → | 2015 | 355 |
| 6 | 2014 | 10 | |
| 7 | The CMU Machine Translation Systems at WMT 2013: Syntax, Synthetic Translation Options, and Pseudo-References | 2013 | 12 |
About Austin Matthews
Austin Matthews is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Language and Linguistics, Infectious Diseases and Organic Chemistry, having authored 7 papers that have together received 401 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (7 papers), Topic Modeling (7 papers), Text Readability and Simplification (2 papers), Speech Recognition and Synthesis (2 papers), Handwritten Text Recognition Techniques (1 paper), Translation Studies and Practices (1 paper) and Multimodal Machine Learning Applications (1 paper). The work is most often cited by research in Artificial Intelligence (355 citations), Computer Vision and Pattern Recognition (82 citations), Information Systems (30 citations), Management Science and Operations Research (11 citations) and Signal Processing (9 citations). Austin Matthews has collaborated with scholars based in United States, Germany and Spain. Frequent co-authors include Chris Dyer, Ling Wang, Miguel Ballesteros, Noah A. Smith, Graham Neubig, Alon Lavie, Waleed Ammar, Xinyi Wang, Michael Denkowski and Matthieu Felix. Their work appears in journals such as Workshop on Statistical Machine Translation and Monash University Research Portal (Monash University).
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.