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Found 16 papers, 9 papers with code
\infty-former: Infinite Memory Transformer
Transformers are unable to model long-term memories effectively, since the amount of computation they need to perform grows with the context length.
Chunk-based Nearest Neighbor Machine Translation
Semi-parametric models, which augment generation with retrieval, have led to impressive results in language modeling and machine translation, due to their ability to retrieve fine-grained information from a datastore of examples.
Efficient Machine Translation Domain Adaptation
On the other hand, semi-parametric models have been shown to successfully perform domain adaptation by retrieving examples from an in-domain datastore (Khandelwal et al., 2021).
Model-Value Inconsistency as a Signal for Epistemic Uncertainty
Unlike prior work which estimates uncertainty by training an ensemble of many models and/or value functions, this approach requires only the single model and value function which are already being learned in most model-based reinforcement learning algorithms.
Model-based Reinforcement Learning
Rolling Shutter Correction
Self-Consistent Models and Values
Learned models of the environment provide reinforcement learning (RL) agents with flexible ways of making predictions about the environment.
$\infty$-former: Infinite Memory Transformer
Transformers are unable to model long-term memories effectively, since the amount of computation they need to perform grows with the context length.
Ranked #1 on
Dialogue Generation
on CMU-DoG
Priberam at MESINESP Multi-label Classification of Medical Texts Task
Information retrieval tools are crucial in order to navigate and provide meaningful recommendations for articles and treatments.
Nova-Wang at SemEval-2020 Task 12: OffensEmblert: An Ensemble ofOffensive Language Classifiers
Our best model, an average ensemble of four different Bert models, achieved 11th place out of 82 participants with a macro F1 score of 0. 91344 in the English SubTask A.
Sparse Text Generation
Current state-of-the-art text generators build on powerful language models such as GPT-2, achieving impressive performance.
Sparse and Structured Visual Attention
Visual attention mechanisms are widely used in multimodal tasks, as visual question answering (VQA).
Joint Learning of Named Entity Recognition and Entity Linking
Named entity recognition (NER) and entity linking (EL) are two fundamentally related tasks, since in order to perform EL, first the mentions to entities have to be detected.
Ranked #10 on
Entity Linking
on AIDA-CoNLL
Hierarchical Nested Named Entity Recognition
In the medical domain and other scientific areas, it is often important to recognize different levels of hierarchy in mentions, such as those related to specific symptoms or diseases associated with different anatomical regions.
Automated Fact Checking in the News Room
Fact checking is an essential task in journalism; its importance has been highlighted due to recently increased concerns and efforts in combating misinformation.
Jointly Extracting and Compressing Documents with Summary State Representations
We present a new neural model for text summarization that first extracts sentences from a document and then compresses them.
Recurrent Predictive State Policy Networks
Predictive state policy networks consist of a recursive filter, which keeps track of a belief about the state of the environment, and a reactive policy that directly maps beliefs to actions, to maximize the cumulative reward.
