Papers with low-resource domains
kNN Retrieval for Simple and Effective Zero-Shot Multi-speaker Text-to-Speech (2025.naacl-short)
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| Challenge: | Neural text-to-speech (TTS) models typically rely on extensive transcribed speech datasets and intricate training pipelines. |
| Approach: | They propose a framework for zero-shot multi-speaker text-to-speech using retrieval methods which leverage the linear relationships between SSL features. |
| Outcome: | The proposed framework achieves comparable performance to state-of-the-art models trained on large training datasets. |
A Reinforcement Learning Approach to Improve Low-Resource Machine Translation Leveraging Domain Monolingual Data (2024.lrec-main)
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| Challenge: | Existing methods for fine-tuning domain adaptation have overfitting problem in low-resource domains . lack of parallel data makes it difficult for model to learn domain-specific knowledge . |
| Approach: | They propose a Reinforcement Learning Domain Adaptation method for Neural Machine Translation that uses in-domain source monolingual data to make up for the lack of parallel data. |
| Outcome: | The proposed method can alleviate overfitting and reinforce the model to learn domain-specific knowledge. |
Discrete Opinion Tree Induction for Aspect-based Sentiment Analysis (2022.acl-long)
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| Challenge: | Dependency trees are used for aspect-based sentiment classification but are not optimized for aspect classification. |
| Approach: | They propose an aspect-specific and language-agnostic discrete latent opinion tree model as an alternative structure to explicit dependency trees. |
| Outcome: | The proposed model can achieve competitive performance and interpretability on six English benchmarks and one Chinese dataset. |
Hierarchical Recurrent Aggregative Generation for Few-Shot NLG (2022.findings-acl)
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| Challenge: | Existing approaches do not account for the fact that some sub-tasks, specifically aggregation and lexicalisation, can benefit from transfer learning in different extents. |
| Approach: | They propose a hierarchical approach for few-shot and zero-shot generation using a three-moduled jointly trained architecture. |
| Outcome: | The proposed approach achieves state-of-the-art on few-shot and zero-shot settings compared to previous approaches. |
SwitchPrompt: Learning Domain-Specific Gated Soft Prompts for Classification in Low-Resource Domains (2023.eacl-main)
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| Challenge: | Recent work shows promising results when prompting pre-trained language models, but in low-resource domains, the domain gap between the pre-training data and the downstream task is too large. |
| Approach: | They propose a method for prompting pre-trained language models using domain-specific keywords with a trainable gated prompt. |
| Outcome: | The proposed prompting method outperforms state-of-the-art prompting methods on three text classification benchmarks and shows that it reduces the need for domain-specific language model pre-training. |
Noisy Label Regularisation for Textual Regression (2022.coling-1)
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| Challenge: | Existing methods to regularise noisy labels are ineffective in the face of noisy data. |
| Approach: | They propose a method that regularises noisy labels and prevents error propagation from the input layer. |
| Outcome: | The proposed method regularises noisy labels and improves generalisation performance over real-world human-disagreement annotations and randomly-corrupted and data-augmented labels. |
DA-Pred: Performance Prediction for Text Summarization under Domain-Shift and Instruct-Tuning (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) often don’t perform as expected under Domain Shift or after Instruct-tuning. |
| Approach: | They propose a method that uses the known performance in high-resource domains and fine-tuning settings to predict performance in low-resourced domains or base models. |
| Outcome: | The proposed method can help researchers decide if resources should be allocated for data labeling and LLM Instruct-tuning. |
A Dataset of Argumentative Dialogues on Scientific Papers (2023.acl-long)
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| Challenge: | Recent advances in question-answering models have made them a great asset in accessing the content of scientific papers. |
| Approach: | They propose to use a dataset of 41 argumentative dialogues between scientists on 20 NLP papers to improve and evaluate their question-answering models. |
| Outcome: | The proposed dataset includes both exploratory and argumentative questions and answers in a dialogue discourse on a scientific paper. |