Reading Comprehension
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Most current question answering datasets frame the task as reading comprehension where the question is about a paragraph or document and the answer often is a span in the document.
Some specific tasks of reading comprehension include multi-modal machine reading comprehension and textual machine reading comprehension, among others. In the literature, machine reading comprehension can be divide into four categories: cloze style, multiple choice, span prediction, and free-form answer. Read more about each category here.
Benchmark datasets used for testing a model's reading comprehension abilities include MovieQA, ReCoRD, and RACE, among others.
The Machine Reading group at UCL also provides an overview of reading comprehension tasks.
Figure source: A Survey on Machine Reading Comprehension: Tasks, Evaluation Metrics and Benchmark Datasets
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/conceptofmind/lamda-rlhf-pytorch
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/contextlab/abstract2paper
https://github.com/cooelf/SG-Net
https://github.com/cooijmanstim/recurrent-batch-normalization
https://github.com/coronazap/bert_client
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/cosmoquester/speech-recognition
https://github.com/cospplay/bert-master
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/crazydigger/Callibration-of-GPT
https://github.com/crx934080895/Bert-CRF_New2
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/csitfun/logiqa2.0
LogiQA: A Challenge Dataset for Machine Reading Comprehension with Logical Reasoning
https://github.com/cstghitpku/GateMemN2N
Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks
https://github.com/cuber2460/bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/cuhksz-nlp/SAPar
XLNet: Generalized Autoregressive Pretraining for Language Understanding
https://github.com/cui0523/Code6/tree/main/albert
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
https://github.com/custodio78/Speech_Recognition
https://github.com/cybertronai/megatron-lm
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/cypressd1999/FYP_2021
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
https://github.com/daltonj/treccastweb
TREC CAsT 2019: The Conversational Assistance Track Overview
https://github.com/dangwalp/qg
https://github.com/danlim-wz/analysis_of_social_media_data_about_singapore
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/darr/nerbert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/darr/nlp_work
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/davidbellamy/labrador
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/davidsvaughn/dts-tf
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/dawn0815/UniSA
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/dbmdz/historic-ner
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/debnsuma/AmazonSageMaker101
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/debnsuma/Intro-Transformer-BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/deepmind/narrativeqa
https://github.com/deepmind/xquad
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/deepmipt/Slavic-BERT-NER
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/deepmipt/bert/tree/feat/multi_gpu
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/deepset-ai/FARM
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/deping-1/2019-false-news-detection-challenge
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/derronxu/sparsebert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.