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Found 11 papers, 3 papers with code
Learning to Revise References for Faithful Summarization
We extract a small corpus from a noisy source--the Electronic Health Record (EHR)--for the task of summarizing a hospital admission from multiple notes.
Rethinking Rehearsal in Lifelong Learning: Does An Example Contribute the Plasticity or Stability?
Traditionally, the primary goal of LL is to achieve the trade-off between the Stability (remembering past tasks) and Plasticity (adapting to new tasks).
Amortized Posterior on Latent Variables in Gaussian Process
Deep neural networks have achieved impressive performance on a variety of domains.
Neural Entity Recognition with Gazetteer based Fusion
Our gazetteer based fusion model is data efficient, achieving +1. 7 micro-F1 gains on the i2b2 dataset using 20% training data, and brings + 4. 7 micro-F1 gains on novel entity mentions never presented during training.
An Empirical Investigation Towards Efficient Multi-Domain Language Model Pre-training
However, in order to achieve state-of-the-art performance on out of domain tasks such as clinical named entity recognition and relation extraction, additional in domain pre-training is required.
Learn to Talk via Proactive Knowledge Transfer
In this paper, we provide an in-depth analysis of KL-divergence minimization in Forward and Backward orders, which shows that learners are reinforced via on-policy learning in Backward.
Proactive Sequence Generator via Knowledge Acquisition
Sequence-to-sequence models such as transformers, which are now being used in a wide variety of NLP tasks, typically need to have very high capacity in order to perform well.
Bidirectional Beam Search: Forward-Backward Inference in Neural Sequence Models for Fill-in-the-Blank Image Captioning
We develop the first approximate inference algorithm for 1-Best (and M-Best) decoding in bidirectional neural sequence models by extending Beam Search (BS) to reason about both forward and backward time dependencies.
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
We observe that our method consistently outperforms BS and previously proposed techniques for diverse decoding from neural sequence models.
SubmodBoxes: Near-Optimal Search for a Set of Diverse Object Proposals
This paper formulates the search for a set of bounding boxes (as needed in object proposal generation) as a monotone submodular maximization problem over the space of all possible bounding boxes in an image.
