Natural Language Processing
 
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Question Answering

2883 papers with code • 130 benchmarks • 360 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 implementations
huggingface/transformers
27 papers
124,984
facebookresearch/ParlAI
5 papers
10,426
dmlc/gluon-nlp
5 papers
2,548
faceonlive/ai-research
5 papers
152
See all 11 libraries.

Datasets

Subtasks

Most implemented papers

Most implemented Social Latest No code

Attention Is All You Need

tensorflow/tensor2tensor • • NeurIPS 2017

The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration.

567
Paper
Code

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

google-research/bert • • NAACL 2019

We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers.

528
Paper
Code

Graph Attention Networks

PetarV-/GAT • • ICLR 2018

We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations.

90
Paper
Code

RoBERTa: A Robustly Optimized BERT Pretraining Approach

pytorch/fairseq • • 26 Jul 2019

Language model pretraining has led to significant performance gains but careful comparison between different approaches is challenging.

59
Paper
Code

Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

huggingface/transformers • • arXiv 2019

Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP).

51
Paper
Code

ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

google-research/ALBERT • • ICLR 2020

Increasing model size when pretraining natural language representations often results in improved performance on downstream tasks.

48
Paper
Code

Language Models are Few-Shot Learners

openai/gpt-3 • NeurIPS 2020

By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do.

48
Paper
Code

Deep contextualized word representations

flairNLP/flair • • NAACL 2018

We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e. g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i. e., to model polysemy).

46
Paper
Code

End-To-End Memory Networks

facebook/MemNN • • NeurIPS 2015

For the former our approach is competitive with Memory Networks, but with less supervision.

44
Paper
Code

LLaMA: Open and Efficient Foundation Language Models

facebookresearch/llama • • arXiv 2023

We introduce LLaMA, a collection of foundation language models ranging from 7B to 65B parameters.

44
Paper
Code
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