Search Results for author:
Found 8 papers, 2 papers with code
Make Templates Smarter: A Template Based Data2Text System Powered by Text Stitch Model
In addition, it reduces human involvement in template design by using a text stitch model to automatically stitch adjacent template units, which is a step that usually requires careful template design and limits template reusability.
NASE: Learning Knowledge Graph Embedding for Link Prediction via Neural Architecture Search
While various forms of models are proposed for the link prediction task, most of them are designed based on a few known relation patterns in several well-known datasets.
Integrating Relation Constraints with Neural Relation Extractors
However, such models often make predictions for each entity pair individually, thus often fail to solve the inconsistency among different predictions, which can be characterized by discrete relation constraints.
Encoding Implicit Relation Requirements for Relation Extraction: A Joint Inference Approach
Relation extraction is the task of identifying predefined relationship between entities, and plays an essential role in information extraction, knowledge base construction, question answering and so on.
Playing 20 Question Game with Policy-Based Reinforcement Learning
The 20 Questions (Q20) game is a well known game which encourages deductive reasoning and creativity.
Marrying up Regular Expressions with Neural Networks: A Case Study for Spoken Language Understanding
The success of many natural language processing (NLP) tasks is bound by the number and quality of annotated data, but there is often a shortage of such training data.
Learning to Predict Charges for Criminal Cases with Legal Basis
The charge prediction task is to determine appropriate charges for a given case, which is helpful for legal assistant systems where the user input is fact description.
Learning with Noise: Enhance Distantly Supervised Relation Extraction with Dynamic Transition Matrix
We show that the dynamic transition matrix can effectively characterize the noise in the training data built by distant supervision.
