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Found 9 papers, 2 papers with code
Single Example Can Improve Zero-Shot Data Generation
We explore two approaches to the generation of task-oriented utterances: in the zero-shot approach, the model is trained to generate utterances from seen intents and is further used to generate utterances for intents unseen during training.
InFoBERT: Zero-Shot Approach to Natural Language Understanding Using Contextualized Word Embedding
Natural language understanding is an important task in modern dialogue systems.
Efficient Grammatical Error Correction Via Multi-Task Training and Optimized Training Schedule
Progress in neural grammatical error correction (GEC) is hindered by the lack of annotated training data.
GEC-DePenD: Non-Autoregressive Grammatical Error Correction with Decoupled Permutation and Decoding
Grammatical error correction (GEC) is an important NLP task that is currently usually solved with autoregressive sequence-to-sequence models.
Sinkhorn Transformations for Single-Query Postprocessing in Text-Video Retrieval
A recent trend in multimodal retrieval is related to postprocessing test set results via the dual-softmax loss (DSL).
PanGu-Σ: Towards Trillion Parameter Language Model with Sparse Heterogeneous Computing
In this work, we develop a system that trained a trillion-parameter language model on a cluster of Ascend 910 AI processors and MindSpore framework, and present the language model with 1. 085T parameters named PanGu-{\Sigma}.
Template-based Approach to Zero-shot Intent Recognition
We outperform previous state-of-the-art f1-measure by up to 16\% for unseen intents, using intent labels and user utterances and without accessing external sources (such as knowledge bases).
A Single Example Can Improve Zero-Shot Data Generation
In the zero-shot approach, the model is trained to generate utterances from seen intents and is further used to generate utterances for intents unseen during training.
Revisiting Mahalanobis Distance for Transformer-Based Out-of-Domain Detection
In turn, the Mahalanobis distance captures this disparity easily.
