Experimentation 1 - Experimental Design (10/04/2022)
Content:
- The scientific method
- Coming up with scientific questions
- Doing a research survey
- Forming research hypotheses
- Running experiments
Reading Material
- Recommended Reading: How to Avoid Machine Learning Pitfalls (Lones 2021)
- Recommended Reading: Best Practices for Data Annotation (Tseng et al. 2020)
- Recommended Viewing: How to Write a Great Research Paper (Peyton-Jones 2006)
- Reference: Sentiment Analysis (Pang et al. 2002)
- Reference: Conversational Question Answering (Reddy et al. 2019)
- Reference: Bottom-up Abstractive Summarization (Gehrmann et al. 2018)
- Reference: Unsupervised Word Segmentation (Kudo and Richardson 2018)
- Reference: Analyzing Language of Bias (Rankin et al. 2017)
- Reference: Are All Languages Equally Hard to Language-Model? (Cotterell et al. 2018)
- Reference: Modeling Podcasts (Reddy et al. 2021)
- Reference: BERT Rediscovers the Classical NLP Pipeline (Tenney et al. 2019)
- Reference: When and Why are Word Embeddings Useful in NMT? (Qi et al. 2018)
- Reference: Kappa Statistic (Carletta 1996)
- Reference: Downside of Surveys (Varian 1994)
- Reference: Penn Treebank Annotation Guidelines (Santoroni 1990)
- Reference: Data Statements for NLP (Bender and Friedman 2018)
- Reference: Power Analysis (Card et al. 2020)
- Reference: Active Learning (Settles 2009)
- Reference: Active Learning Curves (Settles and Craven 2008)
Slides: Experimentation Slides