Question Answering
3011 papers with code • 130 benchmarks • 364 datasets
Question Answering is the task of answering questions (typically reading comprehension questions), but abstaining when presented with a question that cannot be answered based on the provided context.
Question answering can be segmented into domain-specific tasks like community question answering and knowledge-base question answering. Popular benchmark datasets for evaluation question answering systems include SQuAD, HotPotQA, bAbI, TriviaQA, WikiQA, and many others. Models for question answering are typically evaluated on metrics like EM and F1. Some recent top performing models are T5 and XLNet.
( Image credit: SQuAD )
Libraries
Use these libraries to find Question Answering models and implementationsDatasets
Subtasks
- Open-Ended Question Answering
- Open-Domain Question Answering
- Conversational Question Answering
- Answer Selection
- Answer Selection
- Knowledge Base Question Answering
- Community Question Answering
- Zero-Shot Video Question Answer
- Multiple Choice Question Answering (MCQA)
- Long Form Question Answering
- Science Question Answering
- Generative Question Answering
- Cross-Lingual Question Answering
- Mathematical Question Answering
- Temporal/Casual QA
- Logical Reasoning Question Answering
- Multilingual Machine Comprehension in English Hindi
- True or False Question Answering
- Question Quality Assessment
Most implemented papers
BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension
We evaluate a number of noising approaches, finding the best performance by both randomly shuffling the order of the original sentences and using a novel in-filling scheme, where spans of text are replaced with a single mask token.
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
As Transfer Learning from large-scale pre-trained models becomes more prevalent in Natural Language Processing (NLP), operating these large models in on-the-edge and/or under constrained computational training or inference budgets remains challenging.
Distributed Representations of Sentences and Documents
Its construction gives our algorithm the potential to overcome the weaknesses of bag-of-words models.
Bidirectional Attention Flow for Machine Comprehension
Machine comprehension (MC), answering a query about a given context paragraph, requires modeling complex interactions between the context and the query.
Large Batch Optimization for Deep Learning: Training BERT in 76 minutes
In this paper, we first study a principled layerwise adaptation strategy to accelerate training of deep neural networks using large mini-batches.
XLNet: Generalized Autoregressive Pretraining for Language Understanding
With the capability of modeling bidirectional contexts, denoising autoencoding based pretraining like BERT achieves better performance than pretraining approaches based on autoregressive language modeling.
Longformer: The Long-Document Transformer
To address this limitation, we introduce the Longformer with an attention mechanism that scales linearly with sequence length, making it easy to process documents of thousands of tokens or longer.
Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks
One long-term goal of machine learning research is to produce methods that are applicable to reasoning and natural language, in particular building an intelligent dialogue agent.
A simple neural network module for relational reasoning
Relational reasoning is a central component of generally intelligent behavior, but has proven difficult for neural networks to learn.
Pay Attention to MLPs
Transformers have become one of the most important architectural innovations in deep learning and have enabled many breakthroughs over the past few years.