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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/fulstock/mrc_nested_ner_ru

Adaptation of Deep Bidirectional Multilingual Transformers for Russian Language

https://github.com/fuqiang-git-hub/unilmv1-Paddle

Unified Language Model Pre-training for Natural Language Understanding and Generation

https://github.com/fuqianya/ViLBERT-Paddle

ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks

https://github.com/fursovia/self-adj-dice

Dice Loss for Data-imbalanced NLP Tasks

https://github.com/fwbrandao/Abstractive_Summarisation

BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

https://github.com/fywalter/label-bias

Language Models are Few-Shot Learners

https://github.com/g-eklund/bert_tutorial

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

https://github.com/g-k-l/dsi-arxiv-recommender

Distributed Representations of Sentences and Documents

https://github.com/gair-nlp/abel

Solving Quantitative Reasoning Problems with Language Models

https://github.com/galkampel/HyperNetworks

Graph Attention Networks

https://github.com/galsang/ABCNN

ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs

https://github.com/galsang/BiDAF-pytorch

Bidirectional Attention Flow for Machine Comprehension

https://github.com/gammatauai/leetcode-hard-gym

Sparks of Artificial General Intelligence: Early experiments with GPT-4

https://github.com/ganeshjawahar/interpret_bert

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

https://github.com/gangasani-anusha/Short-Story_Assignment

Visual Question Answering: which investigated applications?

https://github.com/ganjinzero/rrhf

LLaMA: Open and Efficient Foundation Language Models

https://github.com/gasolsun36/dynamicrag

APOLLO: An Optimized Training Approach for Long-form Numerical Reasoning

https://github.com/gasolsun36/iter-cot

APOLLO: An Optimized Training Approach for Long-form Numerical Reasoning

https://github.com/gaurav9713/simpleQA_chatbot

End-To-End Memory Networks

https://github.com/gavincaoyuji/NLP

Attention Is All You Need

https://github.com/gayanku/FDGATII

Graph Attention Networks

https://github.com/gbcolborne/lang_id

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

https://github.com/gchhablani/ReCAM

Gated-Attention Readers for Text Comprehension

https://github.com/gchhablani/multilingual-vqa

How Much Can CLIP Benefit Vision-and-Language Tasks?

https://github.com/gcucurull/jax-gat

Graph Attention Networks

https://github.com/gcunhase/embracebert

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

https://github.com/gengchenmai/se-kge

SE-KGE: A Location-Aware Knowledge Graph Embedding Model for Geographic Question Answering and Spatial Semantic Lifting

https://github.com/gentopia-ai/gentopia

Let's Verify Step by Step

https://github.com/geondopark/ckd

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

https://github.com/georgwiese/biomedical-qa

Neural Domain Adaptation for Biomedical Question Answering

https://github.com/geraltofrivia/match-lstm-ptr-network

Machine Comprehension Using Match-LSTM and Answer Pointer

https://github.com/ggerganov/llama.cpp

Language Models are Few-Shot Learners

https://github.com/gggggeun/DeepLearning_Architecture

Attention Is All You Need

https://github.com/ggml-org/llama.cpp

Language Models are Few-Shot Learners

https://github.com/ghazaleh-mahmoodi/lxmert_compression

VizWiz Grand Challenge: Answering Visual Questions from Blind People

https://github.com/ghoshs/counqer

Uncovering Hidden Semantics of Set Information in Knowledge Bases

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