Chapters
Transcript
Episode notes
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
Introduction
00:00 • 5min
The Challenges of Pushing Neural NLG Systems in Production
05:14 • 1min
The Challenges of Evaluating Text Generation Systems
06:30 • 4min
The Cost of Human Evaluation in Text Generation
10:08 • 5min
How to Evaluate a Model Based on Tasks
14:57 • 2min
Extrinsic Evaluations for Voice-Enabled Personal Assistance
17:03 • 3min
How to Evaluate a Machine Learning Model
19:39 • 2min
The Disadvantages of Inter-Annotator Agreement
21:22 • 4min
Should You Push for High Inter-Entered Agreements?
24:57 • 2min
The Importance of Automatic Metrics in Text Generations
27:20 • 2min
The Importance of Quality Metrics in Natural Language Generation
29:35 • 3min
The Importance of Morphings in Machine Translation
32:16 • 2min
The Limits of Machine Translation
34:17 • 2min
The Differences Between Blue and NLTK
36:08 • 2min
How to Use Multiple References in Summarization Tasks
38:21 • 3min
The Role of Rouge in Summarization
41:26 • 2min
The Limitations of Learning Metrics for Text Generation
43:48 • 4min
The Importance of Factual Consistency Measures in Language Generation
47:26 • 5min
The Challenges in Evaluating Extenuation Systems
52:47 • 2min