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Found 9 papers, 3 papers with code
AMR-DA: Data Augmentation by Abstract Meaning Representation
To evaluate the effectiveness of our method, we apply it to the tasks of semantic textual similarity (STS) and text classification.
On Computing Universal Plans for Partially Observable Multi-Agent Path Finding
Given an arbitrary two-dimensional map and a profile of goals for the agents, the system finds a feasible universal plan for each agent that ensures no collision with others.
Using Language Models For Knowledge Acquisition in Natural Language Reasoning Problems
For a natural language problem that requires some non-trivial reasoning to solve, there are at least two ways to do it using a large language model (LLM).
Backward Imitation and Forward Reinforcement Learning via Bi-directional Model Rollouts
Traditional model-based reinforcement learning (RL) methods generate forward rollout traces using the learnt dynamics model to reduce interactions with the real environment.
Generative Adversarial Network
Model-based Reinforcement Learning
+2
PocketNN: Integer-only Training and Inference of Neural Networks via Direct Feedback Alignment and Pocket Activations in Pure C++
In this paper we propose PocketNN, a light and self-contained proof-of-concept framework in pure C++ for the training and inference of DNNs using only integers.
Computing Class Hierarchies from Classifiers
In this paper, we propose a novel algorithm for automatically acquiring a class hierarchy from a classifier which is often a large neural network these days.
Incorporating EDS Graph for AMR Parsing
AMR (Abstract Meaning Representation) and EDS (Elementary Dependency Structures) are two popular meaning representations in NLP/NLU.
XRJL-HKUST at SemEval-2021 Task 4: WordNet-Enhanced Dual Multi-head Co-Attention for Reading Comprehension of Abstract Meaning
This paper presents our submitted system to SemEval 2021 Task 4: Reading Comprehension of Abstract Meaning.
Faster and Safer Training by Embedding High-Level Knowledge into Deep Reinforcement Learning
Deep reinforcement learning has been successfully used in many dynamic decision making domains, especially those with very large state spaces.
