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Found 13 papers, 3 papers with code
LOA: Logical Optimal Actions for Text-based Interaction Games
We present Logical Optimal Actions (LOA), an action decision architecture of reinforcement learning applications with a neuro-symbolic framework which is a combination of neural network and symbolic knowledge acquisition approach for natural language interaction games.
Neuro-Symbolic Reinforcement Learning with First-Order Logic
Deep reinforcement learning (RL) methods often require many trials before convergence, and no direct interpretability of trained policies is provided.
Reinforcement Learning with External Knowledge by using Logical Neural Networks
Specifically, we propose an integrated method that enables model-free reinforcement learning from external knowledge sources in an LNNs-based logical constrained framework such as action shielding and guide.
Polar Embedding
Particularly, the latest approaches such as hyperbolic embeddings showed significant performance by representing essential meanings in a hierarchy (generality and similarity of objects) with spatial properties (distance from the origin and difference of angles).
Structural Supervision Improves Few-Shot Learning and Syntactic Generalization in Neural Language Models
Humans can learn structural properties about a word from minimal experience, and deploy their learned syntactic representations uniformly in different grammatical contexts.
Q-learning with Language Model for Edit-based Unsupervised Summarization
Q-learning is leveraged to train the agent to produce proper edit actions.
Interactive Construction of User-Centric Dictionary for Text Analytics
We propose a methodology to construct a term dictionary for text analytics through an interactive process between a human and a machine, which helps the creation of flexible dictionaries with precise granularity required in typical text analysis.
Image Position Prediction in Multimodal Documents
Datasets for these tasks contain a large number of pairs of an image and the corresponding sentence as an instance.
Dynamic Feature Selection with Attention in Incremental Parsing
One main challenge for incremental transition-based parsers, when future inputs are invisible, is to extract good features from a limited local context.
A neural parser as a direct classifier for head-final languages
This paper demonstrates a neural parser implementation suitable for consistently head-final languages such as Japanese.
Effective Online Reordering with Arc-Eager Transitions
We present a new transition system with word reordering for unrestricted non-projective dependency parsing.
Multilingual Back-and-Forth Conversion between Content and Function Head for Easy Dependency Parsing
Universal Dependencies (UD) is becoming a standard annotation scheme cross-linguistically, but it is argued that this scheme centering on content words is harder to parse than the conventional one centering on function words.
