Papers with supervised settings
How Transferable are Attribute Controllers on Pretrained Multilingual Translation Models? (2024.eacl-long)
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| Challenge: | Pretrained multilingual translation models with massive coverage are becoming of the backbone of many translation systems. |
| Approach: | They propose to use a gradient-based inference-time controller to control a pretrained multilingual model by using a model with attribute annotations. |
| Outcome: | The proposed model performs well on pretrained multilingual models and is attribute- rather than language-specific. |
Unsupervised Deep Language and Dialect Identification for Short Texts (2020.coling-main)
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| Challenge: | Existing methods for identifying closely related short texts are unsupervised . however, performance is poor for unsupervised methods for short texts . |
| Approach: | They propose a method which can learn sentence embeddings and cluster assignments from short texts. |
| Outcome: | The proposed method outperforms state-of-the-art methods in supervised settings . it can learn sentence embeddings and cluster assignments from short texts . |
Unsupervised Fine-tuning for Text Clustering (2020.coling-main)
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| Challenge: | Existing approaches to text clustering fine-tune pre-trained models have been limited. |
| Approach: | They propose a method to fine-tune pre-trained models unsupervisedly for text clustering by learning text representations and cluster assignments using a clustering oriented loss. |
| Outcome: | The proposed model outperforms baseline methods and achieves state-of-the-art results on three text clustering datasets. |
DAGA: Data Augmentation with a Generation Approach for Low-resource Tagging Tasks (2020.emnlp-main)
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Bosheng Ding, Linlin Liu, Lidong Bing, Canasai Kruengkrai, Thien Hai Nguyen, Shafiq Joty, Luo Si, Chunyan Miao
| Challenge: | Data augmentation techniques are widely used to improve machine learning performance . however, due to the complexity of language, it is difficult to generalize such rules for languages. |
| Approach: | They propose a method to generate high quality synthetic data for low-resource tagging tasks . they use unlabeled data only and unlabelled data plus a knowledge base . |
| Outcome: | The proposed method outperforms baselines on NER, part of speech and target based sentiment analysis tasks. |
Extrapolating Multilingual Understanding Models as Multilingual Generators (2023.findings-emnlp)
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| Challenge: | Existing multilingual understanding models are not capable of generating high-quality text compared with decoder-based causal language models. |
| Approach: | They propose a method to adapt a multilingual encoder to a language generator with a small number of additional parameters. |
| Outcome: | The proposed approach outperforms initialization-based methods with 9.4 BLEU on machine translation, 8.1 Rouge-L on question generation, and 5.5 METEOR on story generation. |
Automatic Combination of Sample Selection Strategies for Few-Shot Learning (2026.findings-acl)
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| Challenge: | Existing studies on small language models are characterised by a labelled data scarcity due to data collection/annotation costs or privacy considerations, making the training of typical deep learning models unfeasible. |
| Approach: | They propose a method for Automatic Combination of SamplE Selection Strategies to leverage the strengths and complementarity of various well-established selection objectives. |
| Outcome: | The proposed method outperforms all in-context learning strategies and performs on par or exceeds the in-constinction learning specific baselines. |