HuggingFace
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Krabi, Thailand by Mo Baghdadi

⛵ Learning Meaning in Natural Language Processing — The Semantics Mega-Thread

Full size image here

These map and summary are obviously selective/biased by my personal interests and familiarity with the topics discussed, so if you notice anything wrong or mis-represented please (i) go back to the actual tweets and (ii) send me a message

🏎 A crash course on Lexical Meaning & Semantics

A few pointers: Our simple example came from this nice article by Percy Liang. As a quick overview of the field, I would recommend chapters 12 and 13 of J. Eisenstein’s book “Natural Language Processing”. They will take you through the main ideas, tools up to recent research on Meaning in NLP. Emily M. Bender’s ACL 2018 tutorial is a nice way to see how Meaning can be a multi-headed monster 🐍 to say the least!

🔥 Triggering a debate on Meaning

If all the learner gets is text the learner cannot learn meaning.

Can a model trained only on raw text learn meaning?

The Thai and Java Experiments

Investigating Programming Language Semantic

Language Models

Textual Entailment

An Inductive Bias Language Model

To read more about that, check Yejin Choi’s talk at ACL 2018 and Percy Liang’s talk at AAAI 2018.

The Big Open Question

A word on Searle’s Chinese Room

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Thomas Wolf

Thomas Wolf

Natural Language Processing, Deep learning and Computational Linguistics – Science Lead @Huggingface | thomwolf.io

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