

137 - Nearest Neighbor Language Modeling and Machine Translation, with Urvashi Khandelwal
17 snips Jan 13, 2023
Chapters
Transcript
Episode notes
1 2 3 4 5 6 7 8 9 10 11 12 13 14
Introduction
00:00 • 4min
Can and LM Models - How Do They Work?
03:48 • 4min
The Distribution of Next Words From the Neural Language Model
07:59 • 2min
Is the Canon Model a Better Language Model?
10:21 • 2min
Can and New Language Models Improve Perplexity
12:41 • 2min
Interpolation Hyper Parameter
14:15 • 2min
The Second Experiment in the Paper Was Really Exciting
16:11 • 2min
Do You Think Memorization Could Be Helpful in a Small Model Setting?
18:12 • 2min
Can You Tell Us More About the Extension to Machine Transportation?
19:52 • 2min
KNNLM vs KNNL - What Are the Differences?
21:49 • 3min
How to Train a Machine Translation Model on a Large Number of Language Pairs?
24:22 • 5min
Is It True That You Only Generate Tokens From the Target Language?
29:30 • 2min
Multilingual Machine Translation - What Are Your Takeaways From Your Experiments?
31:19 • 2min
Canon MT Paper - What Are the Most Exciting Open Questions?
32:53 • 3min