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Found 6 papers, 5 papers with code
On the Effectiveness of Sentence Encoding for Intent Detection Meta-Learning
Recent studies on few-shot intent detection have attempted to formulate the task as a meta-learning problem, where a meta-learning model is trained with a certain capability to quickly adapt to newly specified few-shot tasks with potentially unseen intent categories.
CoLaDa: A Collaborative Label Denoising Framework for Cross-lingual Named Entity Recognition
Cross-lingual named entity recognition (NER) aims to train an NER system that generalizes well to a target language by leveraging labeled data in a given source language.
TIARA: Multi-grained Retrieval for Robust Question Answering over Large Knowledge Bases
Pre-trained language models (PLMs) have shown their effectiveness in multiple scenarios.
Disentangling Reasoning Capabilities from Language Models with Compositional Reasoning Transformers
This paper presents ReasonFormer, a unified reasoning framework for mirroring the modular and compositional reasoning process of humans in complex decision-making.
Decomposed Meta-Learning for Few-Shot Named Entity Recognition
Few-shot named entity recognition (NER) systems aim at recognizing novel-class named entities based on only a few labeled examples.
Ranked #5 on
Few-shot NER
on Few-NERD (INTRA)
Issues with Entailment-based Zero-shot Text Classification
The general format of natural language inference (NLI) makes it tempting to be used for zero-shot text classification by casting any target label into a sentence of hypothesis and verifying whether or not it could be entailed by the input, aiming at generic classification applicable on any specified label space.
