Dataset and Model

Table of Contents

• Multi Task

• Reading Comprehension

• Regression

• Semantic Parsing

• Sequence Classification

• Token Classification

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Multi-Task

Dataset

• GLUE Benchmark: The General Language Understanding Evaluation (GLUE) benchmark is a collection of resources for training, evaluating, and analyzing natural language understanding systems.

  • CoLA, MNLI, MRPC, QNLI, QQP, RTE, SST-2, STS-B, WNLI

Model

• Multi-Task Deep Neural Networks for Natural Language Understanding

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

• A Structured Self-attentive Sentence Embedding

• 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

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Regression

• GLUE Benchmark: The General Language Understanding Evaluation (GLUE) benchmark is a collection of resources for training, evaluating, and analyzing natural language understanding systems.

  • STS-B

Model

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

• RoBERTa: A Robustly Optimized BERT Pretraining Approach

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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

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Sequence Classification

Dataset

• GLUE Benchmark: The General Language Understanding Evaluation (GLUE) benchmark is a collection of resources for training, evaluating, and analyzing natural language understanding systems.

  • CoLA, MNLI, MRPC, QNLI, QQP, RTE, SST-2, WNLI

Model

• A Structured Self-attentive Sentence Embedding

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

• RoBERTa: A Robustly Optimized BERT Pretraining Approach

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Token Classification

Dataset

• NER - CoNLL 2013: The shared task of CoNLL-2003 concerns language-independent named entity recognition. Named entities are phrases that contain the names of persons, organizations, locations, times and quantities.

Model

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