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Found 9 papers, 0 papers with code
δ-CAUSAL: Exploring Defeasibility in Causal Reasoning
CESAR achieves a significant 69. 7% relative improvement over existing metrics, increasing from 47. 2% to 80. 1% in capturing the causal strength change brought by supporters and defeaters.
Towards Zero-Shot Personalized Table-to-Text Generation with Contrastive Persona Distillation
Specifically, tabular data and persona information are firstly represented as latent variables separately.
GGP: A Graph-based Grouping Planner for Explicit Control of Long Text Generation
With these two synergic representations, we then regroup these phrases into a fine-grained plan, based on which we generate the final long text.
SPMoE: Generate Multiple Pattern-Aware Outputs with Sparse Pattern Mixture of Experts
Each one-to-one mapping is associated with a conditional generation pattern and is modeled with an expert in SPMoE.
OneStop QAMaker: Extract Question-Answer Pairs from Text in a One-Stop Approach
This pipeline approach, however, is undesired in mining the most appropriate QA pairs from documents since it ignores the connection between question generation and answer extraction, which may lead to incompatible QA pair generation, i. e., the selected answer span is inappropriate for question generation.
Predict-then-Decide: A Predictive Approach for Wait or Answer Task in Dialogue Systems
More specifically, we take advantage of a decision model to help the dialogue system decide whether to wait or answer.
MTSS: Learn from Multiple Domain Teachers and Become a Multi-domain Dialogue Expert
How to build a high-quality multi-domain dialogue system is a challenging work due to its complicated and entangled dialogue state space among each domain, which seriously limits the quality of dialogue policy, and further affects the generated response.
"Wait, I'm Still Talking!" Predicting the Dialogue Interaction Behavior Using Imagine-Then-Arbitrate Model
And the arbitrator decides whether to wait or to make a response to the user directly.
DAL: Dual Adversarial Learning for Dialogue Generation
DAL is the first work to innovatively utilizes the duality between query generation and response generation to avoid safe responses and increase the diversity of the generated responses.
