Dataset and Model

Reading Comprehension

Dataset

• HistoryQA: Joseon History Question Answering Dataset (SQuAD Style)
• KorQuAD: KorQuAD는 한국어 Machine Reading Comprehension을 위해 만든 데이터셋입니다. 모든 질의에 대한 답변은 해당 Wikipedia 아티클 문단의 일부 하위 영역으로 이루어집니다. Stanford Question Answering Dataset(SQuAD) v1.0과 동일한 방식으로 구성되었습니다.
• SQuAD: Stanford Question Answering Dataset is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable.

Model

• BiDAF: Birectional Attention Flow for Machine Comprehension + No Answer
• DrQA: Reading Wikipedia to Answer Open-Domain Questions
• DocQA: Simple and Effective Multi-Paragraph Reading Comprehension + No Answer
• QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension
• BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Semantic Parsing

Dataset

• WikiSQL: A large crowd-sourced dataset for developing natural language interfaces for relational databases. WikiSQL is the dataset released along with our work Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning.

Model

• SQLNet: SQLNet: Generating Structured Queries From Natural Language Without Reinforcement Learning

Sequence Classification

Dataset

Model

• A Structured Self-attentive Sentence Embedding
• BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Token Classification

Dataset

Model

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