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/ZurichNLP/sockeye

Attention Is All You Need

https://github.com/a-rios/ats-models

Longformer: The Long-Document Transformer

https://github.com/a414351664/Bert_MRC

Attention Is All You Need

https://github.com/a414351664/NarrativeQA

Commonsense for Generative Multi-Hop Question Answering Tasks

https://github.com/aaaasssddf/word-embedding-dimensionality-selection

On the Dimensionality of Word Embedding

https://github.com/aadil-srivastava01/End-To-End-Memory-Networks

End-To-End Memory Networks

https://github.com/aananda-giri/gpt2-nepali

https://github.com/aaronH45/sentimentAnalysis

Attention Is All You Need

https://github.com/aaronHuang0121/AI-CUP-2020

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

https://github.com/aaronbae/AnaQA

Reading Wikipedia to Answer Open-Domain Questions

https://github.com/aaronsom/wmt21-qe-tudarmstadt

MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer

https://github.com/aartika/experiment1

Gated-Attention Readers for Text Comprehension

https://github.com/abdumaa/hiqualprop

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/abelriboulot/onnxt5

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

https://github.com/abhaskumarsinha/Corpus2GPT

Language Models are Few-Shot Learners

https://github.com/abhaskumarsinha/Keras-implementation-of-Transformer-Architecture

Attention Is All You Need

https://github.com/abhaskumarsinha/Keras-implementation-of-Transformer-Architecture/blob/main/Transformer.py

Attention Is All You Need

https://github.com/abhaskumarsinha/MinimalGPT

Attention Is All You Need

https://github.com/abhiagg92/bert_sentiment

Attention Is All You Need

https://github.com/abhigoyal1997/CS-763-Project

Exploring Models and Data for Image Question Answering

https://github.com/abhijit-buet/VizWiz-Visua-Question-Answering-2021

A Simple Loss Function for Improving the Convergence and Accuracy of Visual Question Answering Models

https://github.com/abhijit-buet/VizWiz-Visual-Question-Answering-2021

A Simple Loss Function for Improving the Convergence and Accuracy of Visual Question Answering Models

https://github.com/abhilash1910/BERTSimilarity

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

https://github.com/abhilash1910/GraphAttentionNetworks

Graph Attention Networks

https://github.com/abhilashreddys/Fake-News-Article

Attention Is All You Need

https://github.com/abhineet/sentence_classification_pubmed_scibert

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

https://github.com/abhishekanand1710/noiseandbias

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/abhshkdz/House3D

Embodied Question Answering

https://github.com/abmitra84/Language_Modeling

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

https://github.com/abmitra84/Machine_Translation

Attention Is All You Need

https://github.com/adaxry/ss_on_decoding_steps

The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems

https://github.com/adi2103/AML-CoVe

Attention Is All You Need

https://github.com/adriangoe/relational-networks-pytorch

A simple neural network module for relational reasoning

https://github.com/aerinkim/squad_2018

Stochastic Answer Networks for SQuAD 2.0

https://github.com/aethercortex/llama-x

LLaMA: Open and Efficient Foundation Language Models

https://github.com/af-ai-center/bert

Attention Is All You Need

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