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
-
- Multimodal Machine Learning Applications
Papers in
-
- Natural Language Processing Techniques 7
- Topic Modeling 7
- Speech and dialogue systems 7
- Advanced Text Analysis Techniques 1
- AI in Service Interactions 1
-
- Multimodal Machine Learning Applications 1
- Co-authors
- Anusha Balakrishnan (1 shared paper)He He (1 shared paper)Percy Liang (1 shared paper)Dilek Hakkani‐Tür (7 shared papers)Rahul Goel (1 shared paper)Shachi Paul (1 shared paper)Abhishek Sethi (1 shared paper)Sanchit Agarwal (1 shared paper)
- Journals
- Proceedings of the AAAI Conference on Artificial Intelligence (1 paper)arXiv (Cornell University) (2 papers)
- Partner nations
- United States
In The Last Decade
Mihail Eric
9 papers receiving 220 citations
Peers
Comparison fields: 5 of 29
- Artificial Intelligence 225
- Computer Vision and Pattern Recognition 57
- Management Science and Operations Research 15
- 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 | 99 | |
| 2 | MultiWOZ 2.1: Multi-Domain Dialogue State Corrections and State Tracking Baselines | 2019 | 69 |
| 3 | 2020 | 24 | |
| 4 | 2020 | 15 | |
| 5 | Further advances in open domain dialog systems in the Third Alexa Prize Socialbot Grand Challenge | 2020 | 12 |
| 6 | 2021 | 8 | |
| 7 | Beyond Domain APIs: Task-oriented conversational modeling with unstructured knowledge access | 2020 | 4 |
| 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 237 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (7 papers), Topic Modeling (7 papers), Speech and dialogue systems (7 papers), Advanced Text Analysis Techniques (1 paper), AI in Service Interactions (1 paper) and Multimodal Machine Learning Applications (1 paper). The work is most often cited by research in Artificial Intelligence (225 citations), Computer Vision and Pattern Recognition (57 citations), Management Science and Operations Research (15 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, He He, Percy Liang, Dilek Hakkani‐Tür, Rahul Goel, Shachi Paul, Abhishek Sethi, Sanchit Agarwal, Shuyang Gao and Seokhwan Kim. Their work appears in journals such as Proceedings of the AAAI Conference on Artificial Intelligence and arXiv (Cornell 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.