EDEntail: An Entailment-based Few-shot Text Classification with Extensional Definition (2024.findings-naacl)
Copied to clipboard
| Challenge: | Existing methods for few-shot text classification use either class labels or intensional definitions of class labels for label semantics expression. |
| Approach: | They propose a method that employs extensional definition of class labels in hypotheses and then order and format them into a sequence to form hypothese . |
| Outcome: | The proposed method surpasses supervised-learning methods and prompt-based methods on five classification datasets and is comparable to state-of-the-art models. |
Similar Papers
SEE-Few: Seed, Expand and Entail for Few-shot Named Entity Recognition (2022.coling-1)
Copied to clipboard
| Challenge: | Existing few-shot named entity recognition methods focus on leveraging existing datasets in the rich-resource domains which might fail in training-from-scratch setting. |
| Approach: | They propose a multi-task learning framework for Few-shot named entity recognition without using source domain data. |
| Outcome: | The proposed framework outperforms state-of-the-art few-shot named entity recognition methods on a training-from-scratch dataset. |
Universal Natural Language Processing with Limited Annotations: Try Few-shot Textual Entailment as a Start (2020.emnlp-main)
Copied to clipboard
| Challenge: | a current approach to solving NLP problems is to build a problem-specific dataset . current approaches do not allow for transforming tasks into textual entailment . |
| Approach: | They propose a pretrained textual entailment system that can generalize across domains . they argue that when is it worth transforming an NLP task into textual detailment? |
| Outcome: | The proposed model can generalize across domains with few examples, the authors argue . they show that it can be used for several downstream NLP tasks with limited annotations . |
Incremental Few-shot Text Classification with Multi-round New Classes: Formulation, Dataset and System (2021.naacl-main)
Copied to clipboard
| Challenge: | Text classification is usually studied by labeling texts with relevant categories from a predefined set. |
| Approach: | They propose a task where a system incrementally handles multiple rounds of new classes . they propose two entailment approaches, ENTAILMENT and HYBRID, which show promise . |
| Outcome: | The proposed task is based on a few-shot text classification task in the NLP domain. |
ConEntail: An Entailment-based Framework for Universal Zero and Few Shot Classification with Supervised Contrastive Pretraining (2023.eacl-main)
Copied to clipboard
| Challenge: | Existing models for text classification are not universally applicable and lack annotated data. |
| Approach: | They propose a framework for universal zero and few shot classification with supervised contrastive pretraining that can generalize to diverse classification tasks in both zero and many shot settings. |
| Outcome: | The proposed framework outperforms baseline models in zero and few shot settings. |
Automatically Identifying Words That Can Serve as Labels for Few-Shot Text Classification (2020.coling-main)
Copied to clipboard
| Challenge: | Existing approaches to few-shot text classification require domain expertise and an understanding of the language model's abilities to define the mapping between words and labels. |
| Approach: | They propose a method that converts textual inputs to cloze questions that contain some form of task description and processes them with a pretrained language model to map the predicted words to labels. |
| Outcome: | The proposed approach performs almost as well as hand-crafted label-to-word mappings for a number of tasks with small amounts of training data. |
EICO: Improving Few-Shot Text Classification via Explicit and Implicit Consistency Regularization (2022.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for few-shot text classification are limited by labeled data. |
| Approach: | They propose to use consistency regularization to improve few-shot text classification by generating pseudo-labels from weakly-augmented and strongly-augmented views. |
| Outcome: | The proposed method achieves competitive performance with 16 labeled examples with prompt and verbalizer. |
Transductive Learning for Textual Few-Shot Classification in API-based Embedding Models (2023.emnlp-main)
Copied to clipboard
Pierre Colombo, Victor Pellegrain, Malik Boudiaf, Myriam Tami, Victor Storchan, Ismail Ayed, Pablo Piantanida
| Challenge: | Proprietary and closed APIs are impacting the practical applications of natural language processing. |
| Approach: | They propose a scenario where a pre-trained model is served through a gated API . they propose 'transductive inference' that leverages statistics of unlabelled data . |
| Outcome: | The proposed model performs a few-shot classification task with unlabelled data using a gated API . the proposed model can be used to perform the task with a handful of classes . |
Distinct Label Representations for Few-Shot Text Classification (2021.acl-short)
Copied to clipboard
| Challenge: | Existing methods for few-shot text classification ignore the semantic relevance of labels and are difficult to train because of the lack of training examples. |
| Approach: | They propose a method that generates distinct label representations that embed information specific to each label. |
| Outcome: | The proposed method significantly improves few-shot text classification across models and datasets. |
Issues with Entailment-based Zero-shot Text Classification (2021.acl-short)
Copied to clipboard
| Challenge: | Pre-trained BERT models with no fine-tuning can yield competitive performance against BERT fine- tuned for NLI. |
| Approach: | They propose to use any target label into a sentence of hypothesis and verify whether it could be entailed by the input. |
| Outcome: | The proposed models perform better than models fine-tuned for BERT, but the results are in general negative. |
Few-TK: A Dataset for Few-shot Scientific Typed Keyphrase Recognition (2024.findings-naacl)
Copied to clipboard
| Challenge: | Named Entities are a common form of Information Extraction (IE) tasks for scientific texts. |
| Approach: | They propose a rechristening of Named Entities as Typed Keyphrases (TK) they advocate for exploring this task in the few-shot domain due to the scarcity of labeled scientific IE data. |
| Outcome: | The proposed dataset includes scientific Typed Keyphrase annotations on abstracts of 500 research papers. |