| Challenge: | erroneous automatic speech recognition transcriptions and data scarcity hinder spoken QA models . paper focuses on using limited annotated data to improve spoken qa performance . |
| Approach: | They propose a framework for utilizing limited annotated data effectively to improve spoken QA performance. |
| Outcome: | The proposed model produces question-answer pairs from unannotated data with 5.5% relative gain over the model trained with annotated datasets. |
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| Approach: | They propose to use QG module to generate questions from text documents, TTS module to convert text documents into spoken form and automatic speech recognition module to transcribe spoken content. |
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| Challenge: | Question Answering (QA) is a growing area of research . state-of-the-art QA models struggle on out-of domain documents without fine-tuning . |
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Fine-tuning Large Language Models with Limited Data: A Survey and Practical Guide (2026.tacl-1)
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| Challenge: | Pre-trained language models provide strong foundations, but effective adaptation under data scarcity requires efficient and efficient fine-tuning techniques. |
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Towards more equitable question answering systems: How much more data do you need? (2021.acl-short)
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| Challenge: | Question answering datasets in English are relatively new, but lack of linguistic diversity in the field is a challenge. |
| Approach: | They propose to use translation and cross-lingual transfer to produce QA systems in multiple languages to improve their performance. |
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SynDARin: Synthesising Datasets for Automated Reasoning in Low-Resource Languages (2025.coling-main)
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| Challenge: | Question Answering datasets are scarce for languages other than English due to the cost and difficulties of collection and manual annotation. |
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Learning with Limited Data for Multilingual Reading Comprehension (D19-1)
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| Challenge: | Existing approaches to support question answering in a new language with limited training resources introduce noises to the training data due to translation or generation errors. |
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Question-Answering in a Low-resourced Language: Benchmark Dataset and Models for Tigrinya (2023.acl-long)
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| Challenge: | Question-Answering (QA) has seen significant advances in recent years, achieving near human-level performance over some benchmarks. |
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Making More of Little Data: Improving Low-Resource Automatic Speech Recognition Using Data Augmentation (2023.acl-long)
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| Challenge: | Using self-training or text-to-speech (TTS) to improve low-resource ASR performance is costly and can lead to catastrophic forgetting. |
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Accurate Training of Web-based Question Answering Systems with Feedback from Ranked Users (2023.acl-industry)
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| Challenge: | Recent work shows that large-scale annotated datasets are essential for training state-of-the-art Question Answering (QA) models. |
| Approach: | They use large-scale annotated datasets to train question answering models . they use feedback data collected from deployed QA systems to provide cheaper supervision . |
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Chat or Learn: a Data-Driven Robust Question-Answering System (2020.lrec-1)
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| Challenge: | QA systems tend to perform poorly at chitchat, while data-driven chatbots are typically user-friendly but not goal-oriented . |
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