Papers with Stanford Question Answering Dataset
ARES: A Reading Comprehension Ensembling Service (2020.emnlp-demos)
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Anthony Ferritto, Lin Pan, Rishav Chakravarti, Salim Roukos, Radu Florian, J. William Murdock, Avi Sil
| Challenge: | ARES is a machine reading comprehension (MRC) demonstration system which utilizes an ensemble of models to increase F1 by 2.3 points. |
| Approach: | They propose a machine reading comprehension (MRC) demonstration system which utilizes an ensemble of models to increase F1 by 2.3 points. |
| Outcome: | The proposed system increases F1 by 2.3 points on a short answer task using an ensemble of models. |
Answerable or Not: Devising a Dataset for Extending Machine Reading Comprehension (C18-1)
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| Challenge: | Existing MRC algorithms assume that each question is answerable by looking at text passages, but to realize human-like language comprehension ability, a machine should be able to distinguish not-answerable questions from answerable questions. |
| Approach: | They propose a method for automatically assigning difficulty level labels to a dataset that alters an existing MRC dataset and describes the resulting dataset. |
| Outcome: | The proposed method can detect NAQs in a dataset with difficulty level labels and is valid and potentially useful in the development of advanced MRC models. |
Robust Machine Comprehension Models via Adversarial Training (N18-2)
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| Challenge: | Existing models for the Stanford Question Answering Dataset suffer from a 50% decrease in F1 score during adversarial evaluation based on AddSent. |
| Approach: | They propose an alternative adversary-generation algorithm, AddSentDiverse, that significantly increases the variance within the adversarial training data by providing effective examples that punish the model for making certain superficial assumptions. |
| Outcome: | The proposed algorithm can achieve a 36.5% increase in F1 score while maintaining performance on the regular SQuAD task. |
Know What You Don’t Know: Unanswerable Questions for SQuAD (P18-2)
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| Challenge: | Existing datasets focus on answerable questions or use automatically generated unanswerable questions that are easy to identify. |
| Approach: | They propose a dataset that combines the Stanford Question Answering Dataset with 50,000 unanswerable questions written by crowdworkers to look similar to answerable ones. |
| Outcome: | The proposed dataset looks similar to answerable questions on crowd-written questions . strong neural system that gets 86% F1 on SQuAD achieves only 66% F1. |
Stochastic Answer Networks for Machine Reading Comprehension (P18-1)
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| Challenge: | Several recent MRC models employ multi-step reasoning . we show that the use of a stochastic prediction dropout improves robustness . |
| Approach: | They propose a stochastic answer network that simulates multi-step reasoning in machine reading comprehension. |
| Outcome: | The proposed model improves robustness and results competitive with state-of-the-art models on the Stanford Question Answering Dataset and Microsoft MAchine Reading COmprehension Dataset. |
EFTNAS: Searching for Efficient Language Models in First-Order Weight-Reordered Super-Networks (2024.lrec-main)
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| Challenge: | Depending on the size of transformer-based models, they can be restricted from deployment in resource-constrained environments. |
| Approach: | They propose to combine neural architecture search and network pruning techniques to generate and train weight-sharing super-networks that contain efficient transformer-based models. |
| Outcome: | The proposed model achieves high-performing, high-performance subnetworks on the general language understanding evaluation and the Stanford Question Answering Dataset. |
Automatic Spanish Translation of SQuAD Dataset for Multi-lingual Question Answering (2020.lrec-1)
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| Challenge: | Existing methods to train multilingual QA systems are limited for other languages . cross-lingual learning is a technique that transfers knowledge from source to target language with fewer training data. |
| Approach: | They propose a translation method to translate the Stanford Question Answering Dataset to Spanish and a multilingual-BERT model to train Spanish QA systems. |
| Outcome: | The proposed method outperforms the previous benchmarks for cross-lingual extractive QA. |
UQA: Corpus for Urdu Question Answering (2024.lrec-main)
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| Challenge: | Urdu is a low-resource language with over 70 million native speakers . expanding the reach of NLP to languages other than English is crucial for advancing multilingual AI systems. |
| Approach: | They introduce a novel dataset for question answering and text comprehension in Urdu . they use a technique called EATS which preserves the answer spans in translated context paragraphs . |
| Outcome: | The proposed dataset preserves answer spans in translated context paragraphs. |