Yonsei NLP Study Season 2
[About Yonsei NLP Study]
Yonsei NLP Study is consisted of 4 Students who are very passionate in the field of Natural Language Processing!
Every week, each student read papers regarding a certain subject.
Then, each student gets to present about the topic he prepared for.
After the presentation, all the students discuss about that paper and share ideas.
[About Season 2]
Yonsei NLP Study Season 2 (2021.09.07 ~ 2021.11.17)
Main Topic : Question Answering and Various Techniques needed to solve QA Tasks
Among the 4 members, Seungone Kim and Sejune Joo participated in this topic!
[About this repository]
This repository contains presentation materials, links to presentation videos, and a summary of all the papers we have studied in Yonsei NLP Study Season2(2021.09.08~2021.11.17). Our Main Topic was Question Answering and the various techniques needed to solve QA. We have covered 55 papers in total!
About the Members
- Seungone Kim(김승원) - Leader!
- Sejune Joo(주세준)
Link to Presentation(Youtube)
- Taxonomy of QA Datasets and Tasks (Seungone Kim)
- Information Retrieval (Seungone Kim)
- Knowledge Graph & Commonsense Knowledge Graph (Seungone Kim)
- Knowledge Injection & Domain Adaptation (Seungone Kim)
List of Papers we covered
The following are the list of 55 papers
TALK 1 : Question Answering Tasks & Datasets (Seungone Kim)
- Wikiqa : A challenge dataset for open-domain question answering
- SQuAD : 100,000+ Questions for Machine Comprehension of Text
- MS MARCO : A Human Generated MAchine Reading COmprehension Dataset
- TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension
- RACE: Large-scale ReAding Comprehension Dataset From Examinations
- Do We Know What We Don’t Know? Studying Unanswerable Questions beyond SQuAD 2.0
- HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering
- The NarrativeQA Reading Comprehension Challenge
- Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering
- Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning
- DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs
- ELI5: Long Form Question Answering
- SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems
- CoQA: A Conversational Question Answering Challenge
- PubMedQA: A Dataset for Biomedical Research Question Answering
- ANTIQUE: A Non-Factoid Question Answering Benchmark
- ReClor: A Reading Comprehension Dataset Requiring Logical Reasoning
- WinoGrande: An Adversarial Winograd Schema Challenge at Scale
- LogiQA: A Challenge Dataset for Machine Reading Comprehension with Logical Reasoning
TALK 1 : Machine Reading Comprehension (Seungone Kim & Sejune Joo)
- Bidirectional Attention Flow for Machine Comprehension
- FusionNet: Fusing via Fully-Aware Attention with Application to Machine Comprehension
- SG-Net: Syntax-Guided Machine Reading Comprehension
- Retrospective Reader for Machine Reading Comprehension
TALK 2 : Information Retrieval (Seungone Kim)
- ColBERT : Efficient and Effective Passage Search via Contextualized Late Interaction over BERT
- RepBERT : Contextualized Text Embeddings for First-Stage Retrieval
- Dense Passage Retrieval for Open-Domain Question Answering
- COIL : Revisit Exact Lexical Match in Information Retrieval with Contextualized Inverted List
TALK 2 : Information Retrieval & Open Domain QA (Sejune Joo)
- Reading Wikipedia to Answer Open-Domain Questions
- Latent Retrieval for Weakly Supervised Open Domain Question Answering
- REALM: Retrieval-Augmented Language Model Pre-Training
- Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
TALK 3 : Knowledge Graph Completion (Seungone Kim)
- Translating embeddings for modeling multi-relational data
- Knowledge Graph Embedding by Translating on Hyperplanes
- Learning Entity and Relation Embedding for Knowledge Graph Completion
- Embedding Entities and Relations for Learning and Inference in Knowledge Bases
- Complex Embeddings for Simple Link Prediction
- Open-World Knowledge Graph Completion
- Commonsense Knowledge Base Completion
TALK 3 : Commonsense Knowledge Graph Construction (Seungone Kim)
- ConceptNet 5.5: An Open Multilingual Graph of General Knowledge
- ATOMIC: An Atlas of Machine Commonsense for If-Then Reasoning
- COMET: Commonsense Transformers for Automatic Knowledge Graph Construction
TALK 3 : Commonsense Knowledge Graph & QA (Sejune Joo)
TALK 4 : Internal Knowledge Base(LM) vs External Knowledge Base Analysis (Seungone Kim)
- Language Models as Knowledge Bases?
- How Much Knowledge Can You Pack Into the Parameters of a Language Model?
- Birds have four legs?! NumerSense: Probing Numerical Commonsense Knowledge of Pre-trained Language Models
- Improving Question Answering with External Knowledge
- Does External Knowledge Help Explainable Natural Language Inference? Automatic Evaluation vs. Human Ratings
TALK 4 : Domain Adaptation (Seungone Kim)
- SciBERT: A Pretrained Language Model for Scientific Text
- Don't Stop Pretraining: Adapt Language Models to Domains and Tasks
- UDALM: Unsupervised Domain Adaptation through Language Modeling
TALK 4 : Knowledge Injection (Seungone Kim)
- ERNIE: Enhanced Language Representation with Informative Entities
- Knowledge Enhanced Contextual Word Representations
- K-BERT: Enabling Language Representation with Knowledge Graph
- LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention
- KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation