LSTMs Exploit Linguistic Attributes of Data Nelson F . Liu, Omer - PowerPoint PPT Presentation

Jan 08, 2023 •309 likes •416 views

LSTMs Exploit Linguistic Attributes of Data Nelson F . Liu, Omer Levy, Roy Schwartz, Chenhao Tan, Noah A. Smith UWNLP LSTMs work well for natural language data Are they particularly well-suited for language? Testbed Memorization Task

LSTMs Exploit Linguistic Attributes of Data Nelson F . Liu, Omer Levy, Roy Schwartz, Chenhao Tan, Noah A. Smith UWNLP
LSTMs work well for natural language data Are they particularly well-suited for language?
Testbed Memorization Task • Given a constant-length sequence of k inputs, recall the identity of the middle token. • Task is inherently non-linguistic , inputs can be arbitrary sequences. Target [ g m d r p j w f h c ] [ 3 5 6 8 4 0 2 7 9 1 ] [ ! " # ↩ % & ' ( ) * ]
Linguistic Data Improves Memorization Performance
Linguistic Data Improves Memorization Performance
Linguistic Data Improves Memorization Performance
Linguistic Data Improves Memorization Performance
So, are LSTMs particularly well-suited for language? Yes, more than uniform data or data with selected linguistic attributes
LSTMs solve the task by counting
More Questions • How does the LSTM use linguistic patterns in training? • What happens when you add more hidden units? if you want to know more... come to our poster !

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