Not Far Away, Not So Close: Sample Efficient Nearest Neighbour Data Augmentation via MiniMax (2021.findings-acl)
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| Challenge: | Existing kNN-based augmentation techniques blindly incorporate all samples, but MiniMax-kNN uses a subset of augmented samples to maximize KL-divergence between teacher and student models. |
| Approach: | They propose a semi-supervised approach to augmented data augmentation using kNN. |
| Outcome: | The proposed method outperforms existing kNN-based augmentation techniques on several classification tasks and requires fewer augmented examples and less computation to achieve superior performance. |
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| Challenge: | Recent efforts to improve the performance of language models (LMs) have focused on scaling up model and training data size, though with steep accompanying energy and compute resource costs. |
| Approach: | They propose to augment a language model with k-nearest neighbors retrieval on its training data to reduce its perplexity. |
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A Survey of Data Augmentation Approaches for NLP (2021.findings-acl)
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Steven Y. Feng, Varun Gangal, Jason Wei, Sarath Chandar, Soroush Vosoughi, Teruko Mitamura, Eduard Hovy
| Challenge: | Data augmentation is a field of research that has been underexplored due to the discrete nature of language data. |
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An Empirical Survey of Data Augmentation for Limited Data Learning in NLP (2023.tacl-1)
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| Challenge: | Existing methods for enhancing data efficiency in limited labeled data are limited. |
| Approach: | They propose to use data augmentation methods to increase the efficiency of limited data learning in NLP. |
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Nearest Neighbor Zero-Shot Inference (2022.emnlp-main)
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| Challenge: | Using non-parametric memory for retrieval-augmented language models yields significant performance boosts over strong zeroshot baselines. |
| Approach: | They propose a retrieval-augmented language model with fuzzy verbalizers that expands the verbalizes that define different end-task class labels. |
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Rescue Implicit and Long-tail Cases: Nearest Neighbor Relation Extraction (2022.emnlp-main)
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| Challenge: | Existing RE models are incapable of handling implicit expressions and long-tail relation types due to language complexity and data sparsity. |
| Approach: | They propose a method to enhance relation extraction using k nearest neighbors (kNN-RE) kNN is a nearest-neighbor search tool that allows the model to consult training relations at test time . |
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BERT-kNN: Adding a kNN Search Component to Pretrained Language Models for Better QA (2020.findings-emnlp)
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| Challenge: | Pretrained language models (PLMs) capture a diverse range of linguistic and factual knowledge without the use of finetuning. |
| Approach: | They combine BERT with a traditional information retrieval step and a kNN search to improve the recall of facts encountered during training. |
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Contrastive Learning-Enhanced Nearest Neighbor Mechanism for Multi-Label Text Classification (2022.acl-short)
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| Challenge: | Existing methods for multi-label text classification neglect the knowledge from the existing similar instances when predicting labels of a specific text. |
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kNN-BOX: A Unified Framework for Nearest Neighbor Generation (2024.eacl-demo)
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| Challenge: | kNN-BOX enables quick development and visualization for novel generation paradigm . Currently, knn-BOx has provided implementation of seven popular kN-MT variants . |
| Approach: | They propose a framework which decomposes the datastore-augmentation approach into three modules . they apply kNN-BOX to machine translation and three other tasks . |
| Outcome: | The proposed framework decomposes the datastore-augmentation approach into three modules . it provides implementation of seven popular kNN-MT variants, covering research from performance enhancement to efficiency optimization. |
Adaptation Approaches for Nearest Neighbor Language Models (2023.findings-acl)
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| Challenge: | Semi-parametric Nearest Neighbor Language Models (kNN-LMs) have produced impressive gains over purely parametric LMs, however, there has been little investigation into adapting such models for new domains. |
| Approach: | They propose to adapt kNN-LMs to expand neighborhood retrieval over an additional adaptation datastore and adapt the weights of retrieved neighbors using a learned Rescorer module. |
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Combating Label Sparsity in Short Text Topic Modeling via Nearest Neighbor Augmentation (2024.findings-acl)
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| Challenge: | Existing topic models suffer from poor performance when applied to short text contents due to the limited length of a single topic. |
| Approach: | They propose a neural short text topic model that augments reconstruction labels with k-nearest documents to complement relevant but unobserved words. |
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