Papers with Code 아카이브

Question Answering

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하위 태스크: 19
평가표: 153
보존된 평가 기록: 1,781

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 )

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/nanzhaogang/contrib/tree/master/application/a-self-attentive-model-for-knowledge-tracing

A Self-Attentive model for Knowledge Tracing

https://github.com/nastasiaF/5DEEP

Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks

https://github.com/natel9178/transformer-refork

Attention Is All You Need

https://github.com/naver-ai/simseek

Longformer: The Long-Document Transformer

https://github.com/naver/splade

SPLADE: Sparse Lexical and Expansion Model for First Stage Ranking

https://github.com/navodhya/DMN

Ask Me Anything: Dynamic Memory Networks for Natural Language Processing

https://github.com/nballier/IDEEL

Beyond Accuracy: Behavioral Testing of NLP models with CheckList

https://github.com/nbansal90/bAbi_QA

Memory Networks

https://github.com/ncbi-nlp/NCBI_BERT

BioBERT: a pre-trained biomedical language representation model for biomedical text mining

https://github.com/ncbi-nlp/NegBio

ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases

https://github.com/ncoop57/gpt-code-clippy

Language Models are Few-Shot Learners

https://github.com/ncpaddle/splinter-paddlepaddle

Few-Shot Question Answering by Pretraining Span Selection

https://github.com/nebuly-ai/nebullvm/tree/main/apps/accelerate/chatllama

LLaMA: Open and Efficient Foundation Language Models

https://github.com/neburseni/NLP

Attention Is All You Need

https://github.com/necotis/naaqa-acoustic-question-answering

NAAQA: A Neural Architecture for Acoustic Question Answering

https://github.com/nellish123/contrib

Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention

https://github.com/neqkir/attention-mechanism

Attention Is All You Need

https://github.com/nerdimite/bert-finetuning-webinar

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

https://github.com/nerdimite/neuro-symbolic-ai-soc

Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding

https://github.com/nerdimite/relation-network

A simple neural network module for relational reasoning

https://github.com/neuralmagic/lm-evaluation-harness

Language Models are Few-Shot Learners

https://github.com/neuralmind-ai/coliee

DeBERTa: Decoding-enhanced BERT with Disentangled Attention

https://github.com/newmast/QA-Deep-Learning

Making Neural QA as Simple as Possible but not Simpler

https://github.com/nghuyong/ERNIE-Pytorch

ERNIE: Enhanced Representation through Knowledge Integration

https://github.com/ngocphucck/BERT-questions-classification

Attention Is All You Need

https://github.com/nguyenvulebinh/vietnamese-roberta

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/nhatanh81096/Pneumonia-Detector

ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases

https://github.com/nhhoang96/ZeroShotCapsule-PyTorch-

Zero-shot User Intent Detection via Capsule Neural Networks

https://github.com/ni9elf/QANet

QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension

https://github.com/niccolot/ENG-ITA_NMT

Attention Is All You Need

https://github.com/nicholasbao/nlp_job

Attention Is All You Need

https://github.com/nickyeolk/info_retrieve

Learning Semantic Textual Similarity from Conversations

https://github.com/nicolashernandez/readi-lrec22

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

https://github.com/nicolay-r/reasoning-for-sentiment-analysis-framework

Large Language Models are Zero-Shot Reasoners

https://github.com/nicolay-r/thor-ecac

Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

https://github.com/nidhikamal-emb/DPR_repo

Knowledge Guided Text Retrieval and Reading for Open Domain Question Answering

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.