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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/scir-hi/huatuo-llama-med-chinese

Knowledge-tuning Large Language Models with Structured Medical Knowledge Bases for Reliable Response Generation in Chinese

https://github.com/scofield7419/thor-isa

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

https://github.com/scotthlee/injury_autocoding

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

https://github.com/scrayish/ML_NLP

Attention Is All You Need

https://github.com/scut-bds/example_repo_from_scutbds

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

https://github.com/sebastian-hofstaetter/neural-ranking-drmm

A Deep Relevance Matching Model for Ad-hoc Retrieval

https://github.com/secondmind-labs/bayesian_benchmarks

Structured Variational Learning of Bayesian Neural Networks with Horseshoe Priors

https://github.com/see--/natural-question-answering

A BERT Baseline for the Natural Questions

https://github.com/seeyourmind/tkgelib

HittER: Hierarchical Transformers for Knowledge Graph Embeddings

https://github.com/semal/bert

Attention Is All You Need

https://github.com/semantic-systems/NLIWOD

No One is Perfect: Analysing the Performance of Question Answering Components over the DBpedia Knowledge Graph

https://github.com/semantic-web-company/ptlm_wsid

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

https://github.com/semicontinuity/nlp

Attention Is All You Need

https://github.com/seominseok0429/inception-I3D-NON-LOCAL

Attention Is All You Need

https://github.com/seongsikpark/MANN-FPGA

Memory Networks

https://github.com/seraphlabs-ca/SentenceMIM-demo

SentenceMIM: A Latent Variable Language Model

https://github.com/sergeykramp/mthesis-bigbird-embeddings

Big Bird: Transformers for Longer Sequences

https://github.com/seunghwan1228/ELMO

Deep contextualized word representations

https://github.com/seunghwan1228/Transfomer-MachineTranslation

Attention Is All You Need

https://github.com/seungonekim/abductive-commonsense-reasoning

Abductive Commonsense Reasoning

https://github.com/sfu-natlang/SFUTranslate

Scaling Neural Machine Translation

https://github.com/sgkasselau/pyaixi

A Monte Carlo AIXI Approximation

https://github.com/sh951011/Attention-Implementation

Attention Is All You Need

https://github.com/shahrukhx01/ocr-test

Attention Is All You Need

https://github.com/shaikhzhas/bert

Attention Is All You Need

https://github.com/shailaja183/vlqa

Visuo-Linguistic Question Answering (VLQA) Challenge

https://github.com/shailzajolly/Understanding-yesno-and-nonyesno-samples-VQA

P $\approx$ NP, at least in Visual Question Answering

https://github.com/shailzajolly/icdar_vqa

Scene Text Visual Question Answering

https://github.com/shalini1194/SAKT

A Self-Attentive model for Knowledge Tracing

https://github.com/shamalwinchurkar/question-classification

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

https://github.com/shaneding/bilm-tf-experimentation

Deep contextualized word representations

https://github.com/shao-chi/ImageCaption

Attention Is All You Need

https://github.com/shaoxiongji/awesome-knowledge-graph

Knowledge Representation Learning: A Quantitative Review

https://github.com/shark-nlp/openicl

What Makes Good In-Context Examples for GPT-$3$?

https://github.com/shaundsouza/parser-triples

Parser Extraction of Triples in Unstructured Text

https://github.com/shelleyHLX/bilm_EMLo

Deep contextualized word representations

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