thought-provoking

nlp highlights

nlp highlights playlist

Computers are capable of many impressive feats, but making you laugh is usually not one of them. Or could it be? This episode will talk about a custom-built machine learning algorithm that searches through text and writes jokes based on what it finds. 

The jokes are formulaic: they're all of the form "I like my X like I like my Y: Z" where X and Y are nouns, and Z is an adjective that can describe both X and Y. For (dumb) example, "I like my men like I like my coffee: steaming hot." The joke is funny when ZX and ZY are both very common phrases, but X and Y are rarely seen together. 

So, given a large enough corpus of text, the algorithm looks for triplets of words that fit this description and writes jokes based on them. Are the jokes funny? You be the judge...

http://homepages.inf.ed.ac.uk/s0894589/petrovic13unsupervised.pdf

Computers Try to Tell Jokes

A true classic topic in ML: Facial recognition is very high-dimensional, meaning that each picture can have millions of pixels, each of which can be a single feature. It's computationally expensive to deal with all these features, and invites overfitting problems. PCA (principal components analysis) is a classic dimensionality reduction tool that compresses these many dimensions into the few that contain the most variation in the data, and those principal components are often then fed into a classic ML algorithm like and SVM. 

One of the best thing about eigenfaces is the great example code that you can find in sklearn--you can distinguish pictures of world leaders yourself in just a few minutes!

http://scikit-learn.org/stable/auto_examples/applications/face_recognition.html

Facial Recognition with Eigenfaces

So, um... what about machine learning for audio applications?  In the course of starting this podcast, we've edited out a lot of "um"'s from our raw audio files.  It's gotten now to the point that, when we see the waveform in soundstudio, we can almost identify an "um" by eye.  Which makes it an interesting problem for machine learning--is there a way we can train an algorithm to recognize the "um" pattern, too?  This has become a little side project for Katie, which is very much still a work in progress.  We'll talk about what's been accomplished so far, some design choices Katie made in getting the project off the ground, and (of course) mistakes made and hopefully corrected.  We always say that the best way to learn something is by doing it, and this is our chance to try our own machine learning project instead of just telling you about what someone else did! 

Um Detector 1

Project 02 - Sample 1 - Primitive Neural Networks for Generative Music

The reason that neural nets are taking over the world right now is because they can be efficiently trained with the backpropagation algorithm.  In short, backprop allows you to adjust the weights of the neural net based on how good of a job the neural net is doing at classifying training examples, thereby getting better and better at making predictions.  In this episode: we talk backpropagation, and how it makes it possible to train the neural nets we know and love.

Backpropagation

The Rise of Neural Networks and Deep Learning in Our Everyday Lives – w/ Yoshua Bengio

Advances in neural networks are moving fast enough that, even though it seems like we talk about them all the time around here, it also always seems like we're barely keeping up.  So this week we have another installment in our "neural nets: they so smart!" series, talking about three topics.  And all the topics this week were listener suggestions, too!

A Sprint Through What's New in Neural Networks

In this episode we briefly say what we're up to with the podcast.  No technical content, just a description of what each episode will look like, and why we're doing this.

00 - Intro to the podcast

https://www.semanticscholar.org/paper/A-Comparative-Study-of-Word-Embeddings-for-Reading-Dhingra-Liu/3ec37205c9201fc891ab51da200e361fdc34bfb3

01 - A Comparative Study of Word Embeddings for Reading Comprehension

https://www.semanticscholar.org/paper/Bidirectional-Attention-Flow-for-Machine-Seo-Kembhavi/007ab5528b3bd310a80d553cccad4b78dc496b02

02 - Bidirectional Attention Flow for Machine Comprehension

https://www.semanticscholar.org/paper/FastQA-A-Simple-and-Efficient-Neural-Architecture-Weissenborn-Wiese/7c1576b96a1e246d77b30f7b80cec63be96fa698

03 - FastQA: A Simple and Efficient Neural Architecture for Question Answering

An interview with Chris Dyer.

https://www.semanticscholar.org/paper/Recurrent-Neural-Network-Grammars-Dyer-Kuncoro/1594d954abc650bce2db445c52a76e49655efb0c

04 - Recurrent Neural Network Grammars, with Chris Dyer

https://www.semanticscholar.org/paper/Transition-Based-Dependency-Parsing-with-Stack-Lon-Dyer-Ballesteros/396b7932beac62a72288eaea047981cc9a21379a

05 - Transition-Based Dependency Parsing with Stack Long Short-Term Memory

https://www.semanticscholar.org/paper/Design-Challenges-for-Entity-Linking-Ling-Singh/aa2a7ac7bfa9a0201d4faddd4e7bb26299a5e0be

06 - Design Challenges for Entity Linking

https://www.semanticscholar.org/paper/Capturing-Semantic-Similarity-for-Entity-Linking-w-Francis-Landau-Durrett/1c9aca60f7ac5edcceb73d612806704a7d662643

07 - Capturing Semantic Similarity for Entity Linking with Convolutional Neural Networks

https://www.semanticscholar.org/paper/A-Syntactic-Neural-Model-for-General-Purpose-Code-Yin-Neubig/c8d0e13de2eaa09a928eff36b99d63f494c2f5ec

10 - A Syntactic Neural Model for General-Purpose Code Generation

12 - Supervised Learning of Universal Sentence Representations from Natural Language Inference Data

14 - Discourse-Based Objectives for Fast Unsupervised Sentence Representation Learning

15 - Attention and Augmented Recurrent Neural Networks

In this episode, we invite Hal Daumé to continue the discussion on reinforcement learning, focusing on how it has been used in NLP. We discuss how to reduce NLP problems into the reinforcement learning framework, and circumstances where it may or may not be useful. We discuss imitation learning, roll-in and roll-out, and how to approximate an expert with a reference policy. 

DAgger: https://www.semanticscholar.org/paper/A-Reduction-of-Imitation-Learning-and-Structured-to-Ross-Gordon/17eddf33b513ae1134abadab728bdbf6abab2a05?navId=citing-papers
RESLOPE: http://legacydirs.umiacs.umd.edu/~hal/docs/daume18reslope.pdf

75 - Reinforcement / Imitation Learning in NLP, with Hal Daumé III

In this episode, we invite John Hewitt to discuss his take on how to probe word embeddings for syntactic information. The basic idea is to project word embeddings to a vector space where the L2 distance between a pair of words in a sentence approximates the number of hops between them in the dependency tree. The proposed method shows that ELMo and BERT representations, trained with no syntactic supervision, embed many of the unlabeled, undirected dependency attachments between words in the same sentence.

Paper: https://nlp.stanford.edu/pubs/hewitt2019structural.pdf
GitHub repository: https://github.com/john-hewitt/structural-probes
Blog post: https://nlp.stanford.edu/~johnhew/structural-probe.html
Twitter thread: https://twitter.com/johnhewtt/status/1114252302141886464
John's homepage: https://nlp.stanford.edu/~johnhew/

88 - A Structural Probe for Finding Syntax in Word Representations, with John Hewitt

In this episode, we invite Zhou Yu to give an overview of dialogue systems. We discuss different types of dialogue systems (task-oriented vs. non-task-oriented), the main building blocks and how they relate to other research areas in NLP, how to transfer models across domains, and the different ways used to evaluate these systems. Zhou also shares her thoughts on exciting future directions such as developing dialogue methods for non-cooperative environments (e.g., to negotiate prices) and multimodal dialogue systems (e.g., using video as well as audio/text).

Zhou Yu's homepage: http://zhouyu.cs.ucdavis.edu/

89 - Dialog Systems, with Zhou Yu

In this episode, we invite Jonathan Berant to talk about executable semantic parsing. We discuss what executable semantic parsing is and how it differs from related tasks such as semantic dependency parsing and abstract meaning representation (AMR) parsing. We talk about the main components of a semantic parser, how the formal language affects design choices in the parser, and end with a discussion of some exciting open problems in this space.

Jonathan Berant's homepage: http://www.cs.tau.ac.il/~joberant/

91 - (Executable) Semantic Parsing, with Jonathan Berant

In this episode, we invite David Bamman to give an overview of computational humanities. We discuss examples of questions studied in computational humanities (e.g., characterizing fictionality, assessing novelty, measuring the attention given to male vs. female characters in the literature). We talk about the role NLP plays in addressing these questions and how the accuracy and biases of NLP models can influence the results. We also discuss understudied NLP tasks which can help us answer more questions in this domain such as literary scene coreference resolution and constructing a map of literature geography.

David Bamman's homepage: http://people.ischool.berkeley.edu/~dbamman/
LitBank dataset: https://github.com/dbamman/litbank

92 - Computational Humanities, with David Bamman

**The podcast is currently on hiatus. For more active NLP content, check out the Holistic Intelligence Podcast linked below.**

Welcome to the NLP highlights podcast, where we invite researchers to talk about their work in various areas in natural language processing. All views expressed belong to the hosts/guests, and do not represent their employers.

15 - Attention and Augmented Recurrent Neural Networks

NLP Highlights