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/hanzhou032/xqa-dst
공식 코드XQA-DST: Multi-Domain and Multi-Lingual Dialogue State Tracking
https://github.com/harvardnlp/readcomp
공식 코드https://github.com/hclent/creoleval
공식 코드https://github.com/healx/biomed-slot-filling
공식 코드https://github.com/him1411/edgar10q-dataset
공식 코드https://github.com/hint-lab/doctrack
공식 코드https://github.com/hitz-zentroa/latxa
공식 코드Latxa: An Open Language Model and Evaluation Suite for Basque
https://github.com/howardhsu/RCRC
공식 코드https://github.com/htsucml/TiMoS
공식 코드Situation and Behavior Understanding by Trope Detection on Films
https://github.com/hugochan/GraphFlow
공식 코드https://github.com/huminghao16/MTMSN
공식 코드A Multi-Type Multi-Span Network for Reading Comprehension that Requires Discrete Reasoning
https://github.com/huminghao16/RE3QA
공식 코드Retrieve, Read, Rerank: Towards End-to-End Multi-Document Reading Comprehension
https://github.com/hunto/reloss
공식 코드https://github.com/husseinmozannar/SOQAL
공식 코드https://github.com/hybridNLP/look_read_and_enrich
공식 코드Look, Read and Enrich. Learning from Scientific Figures and their Captions
https://github.com/iaar-shanghai/fastmem
공식 코드FastMem: Fast Memorization of Prompt Improves Context Awareness of Large Language Models
https://github.com/ibm-aur-nlp/domain-specific-QA
공식 코드Forget Me Not: Reducing Catastrophic Forgetting for Domain Adaptation in Reading Comprehension
https://github.com/iflytek/minirbt
공식 코드MiniRBT: A Two-stage Distilled Small Chinese Pre-trained Model
https://github.com/iitmnlp/BERT-Analysis-RCQA
공식 코드Towards Interpreting BERT for Reading Comprehension Based QA
https://github.com/ikergarcia1996/noticia
공식 코드NoticIA: A Clickbait Article Summarization Dataset in Spanish
https://github.com/imnearth/autojudger
공식 코드AutoJudger: An Agent-Driven Framework for Efficient Benchmarking of MLLMs
https://github.com/ink-usc/entity-robustness
공식 코드On the Robustness of Reading Comprehension Models to Entity Renaming
https://github.com/internlm/internlm-xcomposer
공식 코드https://github.com/intfloat/commonsense-rc
공식 코드https://github.com/is2ai/kazqad
공식 코드https://github.com/itl-ed/ns-arch
공식 코드https://github.com/jadecxliu/codeqa
공식 코드CodeQA: A Question Answering Dataset for Source Code Comprehension
https://github.com/jb-01/lora-tle
공식 코드Token-Level Adaptation of LoRA Adapters for Downstream Task Generalization
https://github.com/jd-ai-research-nlp/evidr
공식 코드EviDR: Evidence-Emphasized Discrete Reasoning for Reasoning Machine Reading Comprehension
https://github.com/jd-ai-research-nlp/ror
공식 코드RoR: Read-over-Read for Long Document Machine Reading Comprehension
https://github.com/jdeschena/sdtt
공식 코드Beyond Autoregression: Fast LLMs via Self-Distillation Through Time
https://github.com/jenlindadsouza/NLPContributions
공식 코드https://github.com/jhyuklee/DensePhrases
공식 코드https://github.com/jiajiezou/toa
공식 코드Human Attention during Goal-directed Reading Comprehension Relies on Task Optimization
https://github.com/jianguoz/Few-Shot-Intent-Detection
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.