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The Goldilocks Principle: Reading Children's Books with Explicit Memory Representations
Published on May 27, 20162571 Views
We introduce a new test of how well language models capture meaning in children's books. Unlike standard language modelling benchmarks, it distinguishes the task of predicting syntactic function words
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Chapter list
The Goldilocks Principle00:00
Context is needed to understand language - 100:09
Goldilocks - 100:40
Context is needed to understand language - 200:48
Goldilocks - 201:14
Children’s Book Test - 101:27
Children’s Book Test - 201:36
Children’s Book Test - 301:49
Children’s Book Test - 402:01
Children’s Book Test - 502:11
Children’s Book Test - 602:13
Children’s Book Test - 702:16
Children’s Book Test - 802:20
Children’s Book Test - 902:38
CBT: Importance-weighted evaluation03:32
What does the CBT add?04:20
Can humans do the CBT?05:40
Untitled05:52
Query + Context06:06
What about machines?06:23
Performance comparison06:25
Memory Networks for machine reading - 107:21
Memory Networks for machine reading - 207:30
Memory Networks for machine reading - 307:35
Memory Networks for machine reading - 407:44
Memory Networks for machine reading - 507:48
Memory Networks for machine reading - 608:01
Memory Networks for machine reading - 708:08
Memory Networks for machine reading - 808:21
Three ways to represent text in memory08:40
Lexical Memory08:54
Window Memory09:17
Sentence Memory09:37
Self-supervision for memory retrieval09:53
Choose the memory* with the correct answer in it11:15
Results - 111:16
Results - 211:23
DeepMind reading comprehension benchmark - 113:07
DeepMind reading comprehension benchmark - 213:28
Conclusions14:58
Thanks15:57