LocalRQA: From Generating Data to Locally Training, Testing, and Deploying Retrieval-Augmented QA Systems (2024.acl-demos)
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| Challenge: | Existing tools for augmented question-answering do not support researchers and developers to customize the training, testing, and deployment process. |
| Approach: | They propose an open-source toolkit that features a wide selection of model training algorithms, evaluation methods, and deployment tools curated from the latest research. |
| Outcome: | The proposed framework trains and deploys 7B-models with the same performance as OpenAI’s text-ada-002 and GPT-4-turbo. |
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| Challenge: | Existing annotations for other NLP tasks are used to generate domain-specific large-scale question answering (QA) datasets. |
| Approach: | They propose to re-purpose existing annotations for other NLP tasks by generating a large-scale question answering corpus using 1 million questions-logical form and 400,000+ question-answer evidence pairs. |
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Augmenting Pre-trained Language Models with QA-Memory for Open-Domain Question Answering (2023.eacl-main)
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| Challenge: | Existing methods for open-domain question-answering use an open book approach . a recent alternative is to retrieve from a collection of previously-generated question-annwer pairs . |
| Approach: | They propose a new QA system that augments a text-to-text model with a large memory of question-answer pairs and a task for the latent step of question retrieval. |
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Leveraging QA Datasets to Improve Generative Data Augmentation (2022.emnlp-main)
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| Challenge: | Recent advances in NLP have substantially improved the capability of pretrained language models to generate high-quality text. |
| Approach: | They propose to reformulate data generation as context generation for a given question-answer (QA) pair and leverage QA datasets for training context generators. |
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Generation-Augmented Retrieval for Open-Domain Question Answering (2021.acl-long)
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| Challenge: | Existing approaches to answer open-domain questions use sparse representations and sparsity. |
| Approach: | They propose a method which augments a query by generating relevant contexts from heuristically discovered contexts without external supervision. |
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Open Domain Question Answering over Tables via Dense Retrieval (2021.naacl-main)
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| Challenge: | Recent advances in open-domain QA focus on retrieving textual passages . a retriever designed to handle tabular context can improve retrieval quality . |
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Retrieval-Augmented Generative Question Answering for Event Argument Extraction (2022.emnlp-main)
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| Challenge: | Existing methods to extract arguments from documents are based on generating and post-processing a complex target sequence (template). |
| Approach: | They propose a retrieval-augmented generative QA model that retrieves the most similar QA pair and augments it as prompt to the current example's context, then decodes the arguments as answers. |
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A Pipeline for Generating, Annotating and Employing Synthetic Data for Real World Question Answering (2022.emnlp-demos)
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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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NeuralQA: A Usable Library for Question Answering (Contextual Query Expansion + BERT) on Large Datasets (2020.emnlp-demos)
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| Challenge: | Existing tools for Question Answering (QA) have challenges that limit their use in practice. |
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| Outcome: | NeuralQA integrates well with existing infrastructure and offers helpful defaults for QA subtasks. |
PAXQA: Generating Cross-lingual Question Answering Examples at Training Scale (2023.findings-emnlp)
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| Challenge: | Existing question answering systems rely on large, high-quality training data. |
| Approach: | They propose a synthetic data generation method which decomposes cross-lingual QA into two stages . they apply a question generation model to the English side and annotation projection to translate both questions and answers. |
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RocketQA: An Optimized Training Approach to Dense Passage Retrieval for Open-Domain Question Answering (2021.naacl-main)
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Yingqi Qu, Yuchen Ding, Jing Liu, Kai Liu, Ruiyang Ren, Wayne Xin Zhao, Daxiang Dong, Hua Wu, Haifeng Wang
| Challenge: | Open-domain question answering uses dense passage retrieval to find answers . however, it is difficult to effectively train a dual-encoder due to discrepancy between training and inference . |
| Approach: | They propose an optimized training approach to improve dense passage retrieval using RocketQA . they propose cross-batch negatives, denoised hard negatives and data augmentation . |
| Outcome: | The proposed approach outperforms state-of-the-art models on both MSMARCO and Natural Questions. |