Papers with sequence prediction model

2 papers
Generative Bridging Network for Neural Sequence Prediction (N18-1)

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Challenge: Existing approaches to improve the likelihood of sequence prediction models are based on MLE and teacher forcing.
Approach: They propose a Generative Bridging Network (GBN) that extends the point-wise ground truth to a bridge distribution conditioned on it and optimizes their KL-divergence.
Outcome: The proposed bridge module can improve on two recognized sequence prediction tasks and minimize learning burden.
Improving Query Graph Generation for Complex Question Answering over Knowledge Base (2021.emnlp-main)

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Challenge: Existing Knowledge-based Question Answering methods use a query graph to find the answer to a question.
Approach: They propose a method that starts with the entire knowledge base and gradually shrinks it to the desired query graph.
Outcome: Experimental results show that the proposed method achieves state-of-the-art performance on ComplexWebQuestion dataset.

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