Mihail Eric
Impact in
- Artificial Intelligence top 5%
- Topic Modeling
- Speech and dialogue systems
- Natural Language Processing Techniques
- Advanced Graph Neural Networks
- Domain Adaptation and Few-Shot Learning
- AI in Service Interactions
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- Multimodal Machine Learning Applications
Papers in
-
- Topic Modeling 7
- Natural Language Processing Techniques 7
- Speech and dialogue systems 7
- Advanced Text Analysis Techniques 1
- AI in Service Interactions 1
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- Multimodal Machine Learning Applications 1
- Co-authors
- Anusha Balakrishnan (1 shared paper)Percy Liang (1 shared paper)He He (1 shared paper)Dilek Hakkani‐Tür (7 shared papers)Seokhwan Kim (5 shared papers)Rahul Goel (1 shared paper)Sanchit Agarwal (1 shared paper)Shachi Paul (1 shared paper)
- Journals
- arXiv (Cornell University) (2 papers)Proceedings of the AAAI Conference on Artificial Intelligence (1 paper)
- Partner nations
- United States
In The Last Decade
Mihail Eric
9 papers receiving 243 citations
Peers
Comparison fields: 5 of 31
- Artificial Intelligence 247
- Computer Vision and Pattern Recognition 64
- Management Science and Operations Research 16
- Human-Computer Interaction 4
- Information Systems 14
Countries citing papers authored by Mihail Eric
This map shows the geographic impact of Mihail Eric'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 Mihail Eric with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mihail Eric more than expected).
Fields of papers citing papers by Mihail Eric
This network shows the impact of papers produced by Mihail Eric. 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 Mihail Eric. The network helps show where Mihail Eric may publish in the future.
Co-authors
The 25 scholars most cited alongside Mihail Eric, 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 | 2017 | 108 | |
| 2 | MultiWOZ 2.1: Multi-Domain Dialogue State Corrections and State Tracking Baselines | 2019 | 72 |
| 3 | 2020 | 27 | |
| 4 | 2020 | 18 | |
| 5 | Further advances in open domain dialog systems in the Third Alexa Prize Socialbot Grand Challenge | 2020 | 15 |
| 6 | 2021 | 9 | |
| 7 | Beyond Domain APIs: Task-oriented conversational modeling with unstructured knowledge access | 2020 | 5 |
| 8 | 2021 | 4 | |
| 9 | Policy-Driven Neural Response Generation for Knowledge-Grounded Dialogue Systems | 2020 | 2 |
About Mihail Eric
Mihail Eric is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Infectious Diseases, Organic Chemistry and Surgery, having authored 9 papers that have together received 260 indexed citations. Recurring topics across this work include Topic Modeling (7 papers), Natural Language Processing Techniques (7 papers), Speech and dialogue systems (7 papers), Multimodal Machine Learning Applications (1 paper), Advanced Text Analysis Techniques (1 paper) and AI in Service Interactions (1 paper). The work is most often cited by research in Artificial Intelligence (247 citations), Computer Vision and Pattern Recognition (64 citations), Management Science and Operations Research (16 citations), Human-Computer Interaction (4 citations) and Information Systems (14 citations). Mihail Eric has collaborated with scholars based in United States. Frequent co-authors include Anusha Balakrishnan, Percy Liang, He He, Dilek Hakkani‐Tür, Seokhwan Kim, Rahul Goel, Sanchit Agarwal, Shachi Paul, Abhishek Sethi and Shuyang Gao. Their work appears in journals such as arXiv (Cornell University) and Proceedings of the AAAI Conference on Artificial Intelligence.
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.