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Found 8 papers, 2 papers with code
UdS-DFKI@WMT20: Unsupervised MT and Very Low Resource Supervised MT for German-Upper Sorbian
This paper describes the UdS-DFKI submission to the shared task for unsupervised machine translation (MT) and very low-resource supervised MT between German (de) and Upper Sorbian (hsb) at the Fifth Conference of Machine Translation (WMT20).
The University of Maryland, College Park Submission to Large-Scale Multilingual Shared Task at WMT 2021
This paper describes the system submitted to Large-Scale Multilingual Shared Task (Small Task #2) at WMT 2021.
Natural Language Response Generation from SQL with Generalization and Back-translation
Generation of natural language responses to the queries of structured language like SQL is very challenging as it requires generalization to new domains and the ability to answer ambiguous queries among other issues.
On the Complexity of Learning to Cooperate with Populations of Socially Rational Agents
Artificially intelligent agents deployed in the real-world will require the ability to reliably \textit{cooperate} with humans (as well as other, heterogeneous AI agents).
JaxMARL: Multi-Agent RL Environments in JAX
This not only enables GPU acceleration, but also provides a more flexible MARL environment, unlocking the potential for self-play, meta-learning, and other future applications in MARL.
Targets in Reinforcement Learning to solve Stackelberg Security Games
Reinforcement Learning (RL) algorithms have been successfully applied to real world situations like illegal smuggling, poaching, deforestation, climate change, airport security, etc.
Improving Question Answering with Generation of NQ-like Questions
We demonstrate that in a low resource setting, using the generated data improves the QA performance over the baseline system on both NQ and QB data.
Read, Highlight and Summarize: A Hierarchical Neural Semantic Encoder-based Approach
In this paper, we propose a method based on extracting the highlights of a document; a key concept that is conveyed in a few sentences.
