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/dngback/co-forget-protocol

DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

https://github.com/doc-doc/NExT-OE

NExT-QA:Next Phase of Question-Answering to Explaining Temporal Actions

https://github.com/docarray/docarray

Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

https://github.com/dodo47/SpikE

Neuro-symbolic computing with spiking neural networks

https://github.com/domluna/memn2n

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

https://github.com/dongquan11/SOPPP_CB_and_HS_games

Path Planning Problems with Side Observations-When Colonels Play Hide-and-Seek

https://github.com/dqqcasia/st

Attention Is All You Need

https://github.com/dreamgonfly/BERT-pytorch

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

https://github.com/dreamgonfly/Transformer-pytorch

Attention Is All You Need

https://github.com/dreamnotover/english_chinese_machine_translation_baseline

Attention Is All You Need

https://github.com/dreji18/Tapas-Implementation

TAPAS: Weakly Supervised Table Parsing via Pre-training

https://github.com/drumpt/Transformer

Attention Is All You Need

https://github.com/ds4dm/sGat

Graph Attention Networks

https://github.com/ds4dm/sparse-gcn

Graph Attention Networks

https://github.com/dsouzadaniel/BiDAF

Bidirectional Attention Flow for Machine Comprehension

https://github.com/duanchi1230/NLP_Project_AI2_Reasoning_Challenge

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/dujiaxin/Knowledge-Aware-Reader

Improving Question Answering over Incomplete KBs with Knowledge-Aware Reader

https://github.com/dujiaxin/semanticRetrievalMRS

Revealing the Importance of Semantic Retrieval for Machine Reading at Scale

https://github.com/duxiaoman-di/xuanyuan

Self-QA: Unsupervised Knowledge Guided Language Model Alignment

https://github.com/duyunshu/bert-sentiment-analysis

Attention Is All You Need

https://github.com/dwiggles/AIX360-withdata

Leveraging Latent Features for Local Explanations

https://github.com/dyq0811/EEG-Transformer-seq2seq

Attention Is All You Need

https://github.com/dzb1998/pyGAT

Graph Attention Networks

https://github.com/dzqjorking/transpose

Attention Is All You Need

https://github.com/eagle705/bert

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

https://github.com/eaishwa/quora-ques-pair-similarity

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

https://github.com/ecnu-icalk/educhat

LLaMA: Open and Efficient Foundation Language Models

https://github.com/ecolab-postech/owq

LLaMA: Open and Efficient Foundation Language Models

https://github.com/edchengg/transfusion

Frustratingly Easy Label Projection for Cross-lingual Transfer

https://github.com/edithal-14/DMN-Novelty

Dynamic Memory Networks for Visual and Textual Question Answering

https://github.com/ednussi/thesis_public

Few-Shot Question Answering by Pretraining Span Selection

https://github.com/efeslab/fiddler

Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity

https://github.com/efficientqa/retrieval-based-baselines

Dense Passage Retrieval for Open-Domain Question Answering

https://github.com/egillian1/AblationPLATO

PLATO: Pre-trained Dialogue Generation Model with Discrete Latent Variable

https://github.com/eladsegal/project-NLP-AML

A Simple and Effective Model for Answering Multi-span Questions

https://github.com/eleanorye00/Gamifier

Attention Is All You Need

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