| Challenge: | Existing QA datasets only available for limited domains and languages. |
| Approach: | They propose to generate context, question and answer triples in an unsupervised manner and synthesize extractive QA training data automatically. |
| Outcome: | The proposed approach outperforms existing QA models on a common EQA benchmark dataset. |
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Simple and Effective Semi-Supervised Question Answering (N18-2)
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| Challenge: | Existing deep learning systems for extractive Question Answering are limited and expensive to construct. |
| Approach: | They propose a semi-supervised QA system where end user specifies a set of documents and only a few labelled examples. |
| Outcome: | The proposed system achieves 50% F1 score on SQuAD and TriviaQA with very little labeled data. |
Handling Anomalies of Synthetic Questions in Unsupervised Question Answering (2020.coling-main)
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| Challenge: | Existing approaches to improve unsupervised Question Answering (UQA) are expensive and require additional datasets. |
| Approach: | They propose an unsupervised QA approach that generates QA training data automatically. |
| Outcome: | The proposed method improves unsupervised QA significantly across a number of QA tasks. |
Template-Based Question Generation from Retrieved Sentences for Improved Unsupervised Question Answering (2020.acl-main)
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| Challenge: | Question Answering (QA) is a field of increasing demand due to the availability of information online. |
| Approach: | They propose an unsupervised approach to training QA models with generated pseudo-training data by applying a simple template on a related sentence rather than the original context sentence. |
| Outcome: | The proposed approach improves the performance of a QA model on generated pseudo-training data. |
Harvesting and Refining Question-Answer Pairs for Unsupervised QA (2020.acl-main)
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| Challenge: | Recent research attempts to extend unsupervised question answering to settings with few or no labeled data available. |
| Approach: | They propose two approaches to improve unsupervised question answering . first, they harvest lexically and syntactically divergent Wikipedia questions to automatically construct a corpus of question-answer pairs . second, they take advantage of the QA model to extract more appropriate answers . |
| Outcome: | The proposed approach outperforms previous unsupervised approaches by a large margin and is competitive with early supervised models. |
Improving Unsupervised Question Answering via Summarization-Informed Question Generation (2021.emnlp-main)
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| Challenge: | Question Generation (QG) is the production of meaningful questions given a set of input passages and corresponding answers. |
| Approach: | They propose a method which uses questions generated heuristically from news summaries as a source of training data for a QG system. |
| Outcome: | The proposed method outperforms previous unsupervised models on three in-domain datasets and three out-of-domain ones. |
Unsupervised Adaptation of Question Answering Systems via Generative Self-training (2020.emnlp-main)
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| Challenge: | Supervised self-training methods have transformed applied machine learning . however, adapting to target data has received little attention . |
| Approach: | They propose a method to generate synthetic QA pairs for unsupervised self adaptation . they use massive amounts of data to simulate self-supervised tasks . |
| Outcome: | The proposed method improves QA systems significantly by using less data and training computation than existing augmentation approaches. |
Exploiting Cloze-Questions for Few-Shot Text Classification and Natural Language Inference (2021.eacl-main)
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| Challenge: | Existing approaches to learning from examples are limited due to the vast number of languages, domains and tasks. |
| Approach: | They propose a semi-supervised training procedure that reformulates input examples as cloze-style phrases to help language models understand a given task. |
| Outcome: | The proposed approach outperforms supervised training and strong semi-supervised approaches in low-resource settings by a large margin. |
QuASE: Question-Answer Driven Sentence Encoding (2020.acl-main)
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| Challenge: | Question-answering (QA) data often encodes essential information in many facets . a growing interest of QA has led to many large-scale QA datasets available to the community . |
| Approach: | They propose a question-answer driven sentence encoding framework to learn representations from QA data. |
| Outcome: | The proposed framework learns representations from QA data, using BERT or other state-of-the-art contextual language models. |
Reference Free Domain Adaptation for Translation of Noisy Questions with Question Specific Rewards (2023.findings-emnlp)
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Baban Gain, Ramakrishna Appicharla, Soumya Chennabasavaraj, Nikesh Garera, Asif Ekbal, Muthusamy Chelliah
| Challenge: | Creating a synthetic parallel corpus from noisy data is also difficult due to its noisy nature. |
| Approach: | They propose a training methodology that fine-tunes the NMT system only using source-side data to balance adequacy and fluency. |
| Outcome: | The proposed method surpasses the MLE-based fine-tuning approach by achieving a 1.9 BLEU improvement. |
Question Answering Infused Pre-training of General-Purpose Contextualized Representations (2022.findings-acl)
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| Challenge: | Existing pretraining objectives for question answering (QA) are not optimized for being immediately useful without fine-tuning. |
| Approach: | They propose a pre-training objective based on question answering (QA) that is based more directly on context. |
| Outcome: | The proposed model matches predictions of a more accurate cross-encoder model on 80 million synthesized QA pairs and achieves large improvements over previous state-of-the-art models on paraphrase detection and fewshot named entity recognition. |