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Found 10 papers, 7 papers with code
C-PMI: Conditional Pointwise Mutual Information for Turn-level Dialogue Evaluation
Existing reference-free turn-level evaluation metrics for chatbots inadequately capture the interaction between the user and the system.
Sparse Modular Activation for Efficient Sequence Modeling
To validate the effectiveness of SMA on sequence modeling, we design a novel neural architecture, SeqBoat, which employs SMA to sparsely activate a Gated Attention Unit (GAU) based on the state representations learned from an SSM.
Ranked #2 on
Long-range modeling
on LRA
Language Model Pre-Training with Sparse Latent Typing
Modern large-scale Pre-trained Language Models (PLMs) have achieved tremendous success on a wide range of downstream tasks.
Ranked #6 on
Few-shot NER
on Few-NERD (INTRA)
(using extra training data)
HySPA: Hybrid Span Generation for Scalable Text-to-Graph Extraction
Text-to-Graph extraction aims to automatically extract information graphs consisting of mentions and types from natural language texts.
Ranked #1 on
Relation Extraction
on ACE 2005
(Sentence Encoder metric)
A Simple Fix for Convolutional Neural Network via Coordinate Embedding
Thus the generalization ability of CNN will be limited since the coordinate information is crucial for a model to learn affine transformations which directly operate on the coordinate of each pixel.
RoNGBa: A Robustly Optimized Natural Gradient Boosting Training Approach with Leaf Number Clipping
Natural gradient has been recently introduced to the field of boosting to enable the generic probabilistic predication capability.
Scalable and Accurate Dialogue State Tracking via Hierarchical Sequence Generation
Experiments on both the multi-domain and the single domain dialogue state tracking dataset show that our model not only scales easily with the increasing number of pre-defined domains and slots but also reaches the state-of-the-art performance.
Ranked #14 on
Multi-domain Dialogue State Tracking
on MULTIWOZ 2.0
Dialogue State Tracking
Multi-domain Dialogue State Tracking
Towards Universal Dialogue State Tracking
Dialogue state tracking is the core part of a spoken dialogue system.
Cost-Sensitive Active Learning for Dialogue State Tracking
Dialogue state tracking (DST), when formulated as a supervised learning problem, relies on labelled data.
Recurrent Soft Attention Model for Common Object Recognition
We propose the Recurrent Soft Attention Model, which integrates the visual attention from the original image to a LSTM memory cell through a down-sample network.
