Data Skeptic: Natural Language Processing

NLP in 2019

A year in recap. ... [more]


The Limits of NLP

We are joined by Colin Raffel to discuss the paper "Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer". ... [more]


Ancient Text Restoration

Thea Sommerschield joins us this week to discuss the development of Pythia - a machine learning model trained to assist in the reconstruction of ancient language text. ... [more]


Jumpstart Your ML Project

Seth Juarez joins us to discuss the toolbox of options available to a data scientist to jumpstart or extend their machine learning efforts. ... [more]


ML Ops

Kyle met up with Damian Brady at MS Ignite 2019 to discuss machine learning operations. ... [more]


Serverless NLP Model Training

Alex Reeves joins us to discuss some of the challenges around building a serverless, scalable, generic machine learning pipeline.  The is a technical deep dive on architecting solutions and a discussion of some of the design choices made. ... [more]


Team Data Science Process

Buck Woody joins Kyle to share experiences from the field and the application of the Team Data Science Process - a popular six-phase workflow for doing data science.   ... [more]


Annotator Bias

The modern deep learning approaches to natural language processing are voracious in their demands for large corpora to train on.  Folk wisdom estimates used to be around 100k documents were required for effective training.  The availability of broadly trained, general-purpose models like BERT has made it possible to do transfer learning to achieve novel results on much smaller corpora. Thanks to these advancements, an NLP researcher might get value out of fewer examples since they can use the transfer learning to get a head start and focus on learning the nuances of the language specifically relevant to the task at hand.  Thus, small specialized corpora are both useful and practical to create. In this episode, Kyle speaks with Mor Geva, lead author on the recent paper Are We Modeling the Task or the Annotator? An Investigation of Annotator Bias in Natural Language Understanding Datasets, which explores some unintended consequences of the typical procedure followed for generating corpora. Source code for the paper available here: https://github.com/mega002/annotator_bias   ... [more]


Indigenous American Language Research

Manuel Mager joins us to discuss natural language processing for low and under-resourced languages.  We discuss current work in this area and the Naki Project which aggregates research on NLP for native and indigenous languages of the American continent. ... [more]


NLP for Developers

While at MS Build 2019, Kyle sat down with Lance Olson from the Applied AI team about how tools like cognitive services and cognitive search enable non-data scientists to access relatively advanced NLP tools out of box, and how more advanced data scientists can focus more time on the bigger picture problems. ... [more]


Reproducing Deep Learning Models

Rajiv Shah attempted to reproduce an earthquake-predicting deep learning model.  His results exposed some issues with the model.  Kyle and Rajiv discuss the original paper and Rajiv\'s analysis. ... [more]


Talking to GPT-2

GPT-2 is yet another in a succession of models like ELMo and BERT which adopt a similar deep learning architecture and train an unsupervised model on a massive text corpus. As we have been covering recently, these approaches are showing tremendous promise, but how close are they to an AGI?  Our guest today, Vazgen Davidyants wondered exactly that, and have conversations with a Chatbot running GPT-2.  We discuss his experiences as well as some novel thoughts on artificial intelligence. ... [more]


Applied Data Science in Industry

Kyle sits down with Jen Stirrup to inquire about her experiences helping companies deploy data science solutions in a variety of different settings. ... [more]


BERT is Magic

Kyle pontificates on how impressed he is with BERT. ... [more]


BERT is Shallow

Tim Niven joins us this week to discuss his work exploring the limits of what BERT can do on certain natural language tasks such as adversarial attacks, compositional learning, and systematic learning. ... [more]


SpanBERT

Omer Levy joins us to discuss "SpanBERT: Improving Pre-training by Representing and Predicting Spans". https://arxiv.org/abs/1907.10529 ... [more]


What BERT is Not

Allyson Ettinger joins us to discuss her work in computational linguistics, specifically in exploring some of the ways in which the popular natural language processing approach BERT has limitations. ... [more]


BERT

Kyle provides a non-technical overview of why Bidirectional Encoder Representations from Transformers (BERT) is a powerful tool for natural language processing projects. ... [more]


Building the howto100m Video Corpus

Video annotation is an expensive and time-consuming process. As a consequence, the available video datasets are useful but small. The availability of machine transcribed explainer videos offers a unique opportunity to rapidly develop a useful, if dirty, corpus of videos that are "self annotating", as hosts explain the actions they are taking on the screen. This episode is a discussion of the HowTo100m dataset - a project which has assembled a video corpus of 136M video clips with captions covering 23k activities. Related Links The paper will be presented at ICCV 2019 @antoine77340 Antoine on Github Antoine\'s homepage ... [more]


Catastrophic Forgetting

Kyle and Linhda discuss some high level theory of mind and overview the concept machine learning concept of catastrophic forgetting. ... [more]


Facebook Bargaining Bots Invented a Language

In 2017, Facebook published a paper called Deal or No Deal? End-to-End Learning for Negotiation Dialogues. In this research, the reinforcement learning agents developed a mechanism of communication (which could be called a language) that made them able to optimize their scores in the negotiation game. Many media sources reported this as if it were a first step towards Skynet taking over. In this episode, Kyle discusses bargaining agents and the actual results of this research. ... [more]


Onnx

Kyle interviews Prasanth Pulavarthi about the Onnx format for deep neural networks. ... [more]


Transfer Learning

Sebastian Ruder is a research scientist at DeepMind.  In this episode, he joins us to discuss the state of the art in transfer learning and his contributions to it. ... [more]


Data Infrastructure in the Cloud

Kyle chats with Rohan Kumar about hyperscale, data at the edge, and a variety of other trends in data engineering in the cloud. ... [more]


Named Entity Recognition

Kyle and Linh Da discuss the class of approaches called "Named Entity Recognition" or NER.  NER algorithms take any string as input and return a list of "entities" - specific facts and agents in the text along with a classification of the type (e.g. person, date, place). ... [more]


Neural Turing Machines

Kyle and Linh Da discuss the concepts behind the neural Turing machine. ... [more]


The Death of a Language

USC students from the CAIS++ student organization have created a variety of novel projects under the mission statement of "artificial intelligence for social good". In this episode, Kyle interviews Zane and Leena about the Endangered Languages Project. ... [more]


Under Resourced Languages

Priyanka Biswas joins us in this episode to discuss natural language processing for languages that do not have as many resources as those that are more commonly studied such as English.  Successful NLP projects benefit from the availability of like large corpora, well-annotated corpora, software libraries, and pre-trained models.  For languages that researchers have not paid as much attention to, these tools are not always available. ... [more]


Attention Primer

A gentle introduction to the very high-level idea of "attention" in machine learning, as it will play a major role in some upcoming episodes over the next few weeks. ... [more]


Mapping Dialects with Twitter Data

When users on Twitter post with geographic tags, it creates the opportunity for a variety of interesting questions to be posed having to do with language, dialects, and location.  In this episode, Kyle interviews Bruno Gonçalves about his work studying language in this way.   ... [more]


NCAA Predictions on Spark

In this episode, Kyle interviews Laura Edell at MS Build 2019.  The conversation covers a number of topics, notably her NCAA Final 4 prediction model.   ... [more]


Sentiment Analysis

This is an interview with Ellen Loeshelle, Director of Product Management at Clarabridge.  We primarily discuss sentiment analysis. ... [more]


The Transformer

Kyle and Linhda discuss attention and the transformer - an encoder/decoder architecture that extends the basic ideas of vector embeddings like word2vec into a more contextual use case. ... [more]


BLEU

Bilingual evaluation understudy (or BLEU) is a metric for evaluating the quality of machine translation using human translation as examples of acceptable quality results. This metric has become a widely used standard in the research literature. But is it the perfect measure of quality of machine translation? ... [more]


Cross-lingual Short-text Matching

Modern messaging technology has facilitated a trend towards highly compact, short messages send by users who can presume a great amount of context held between the communicating parties.  The rules of grammar may be discarded and often visible errors are a normal part of the conversation. >>> Good mornink >>> morning Yet such short messages are also important for businesses whose users are unlikely to read a large block of text upon completing an order.  Similarly, a business might want to offer assistance and effective question and answering solutions in an automated and ideally multi-lingual way.  In this episode, we discuss techniques for designing solutions like that.   ... [more]


ELMo

ELMo (Embeddings from Language Models) introduced the idea of deep contextualized word representations. It extends previous ideas like word2vec and GloVe. The ELMo model is a neural network able to map natural language into a vector space. This vector space, out of box, proved to be incredibly useful in a wide variety of seemingly unrelated NLP tasks like sentiment analysis and name entity recognition. ... [more]


Human vs Machine Transcription

Machine transcription (the process of translating audio recordings of language to text) has come a long way in recent years. But how do the errors made during machine transcription compare to the errors made by a human transcriber? Find out in this episode! ... [more]


Recurrent Relational Networks

One of the most challenging NLP tasks is natural language understanding and reasoning. How can we construct algorithms that are able to achieve human level understanding of text and be able to answer general questions about it? This is truly an open problem, and one with the bAbI dataset has been constructed to facilitate. bAbI presents a variety of different language understanding and reasoning tasks and exists as benchmark for comparing approaches. In this episode, Kyle talks to Rasmus Berg Palm about his recent paper Recurrent Relational Networks ... [more]


seq2seq

A sequence to sequence (or seq2seq) model is neural architecture used for translation (and other tasks) which consists of an encoder and a decoder. The encoder/decoder architecture has obvious promise for machine translation, and has been successfully applied this way. Encoding an input to a small number of hidden nodes which can effectively be decoded to a matching string requires machine learning to learn an efficient representation of the essence of the strings. In addition to translation, seq2seq models have been used in a number of other NLP tasks such as summarization and image captioning. Related Links tf-seq2seq Describing Multimedia Content using Attention-based Encoder--Decoder Networks Show and Tell: A Neural Image Caption Generator Attend to You: Personalized Image Captioning with Context Sequence Memory Networks ... [more]


Simultaneous Translation at Baidu

While at NeurIPS 2018, Kyle chatted with Liang Huang about his work with Baidu research on simultaneous translation, which was demoed at the conference. ... [more]


Text Mining in R

Kyle interviews Julia Silge about her path into data science, her book Text Mining with R, and some of the ways in which she\'s used natural language processing in projects both personal and professional. Related Links https://stack-survey-2018.glitch.me/ https://stackoverflow.blog/2017/03/28/realistic-developer-fiction/ ... [more]


Authorship Attribution

In a recent paper, Leveraging Discourse Information Effectively for Authorship Attribution, authors Su Wang, Elisa Ferracane, and Raymond J. Mooney describe a deep learning methodology for predict which of a collection of authors was the author of a given document. ... [more]


Very Large Corpora and Zipf\'s Law

The earliest efforts to apply machine learning to natural language tended to convert every token (every word, more or less) into a unique feature. While techniques like stemming may have cut the number of unique tokens down, researchers always had to face a problem that was highly dimensional. Naive Bayes algorithm was celebrated in NLP applications because of its ability to efficiently process highly dimensional data. Of course, other algorithms were applied to natural language tasks as well. While different algorithms had different strengths and weaknesses to different NLP problems, an early paper titled Scaling to Very Very Large Corpora for Natural Language Disambiguation popularized one somewhat surprising idea. For many NLP tasks, simply providing a large corpus of examples not only improved accuracy, but it also showed that asymptotically, some algorithms yielded more improvement from working on very, very large corpora. Although not explicitly in about NLP, the noteworthy paper The Unreasonable Effectiveness of Data emphasizes this point further while paying homage to the classic treatise The Unreasonable Effectiveness of Mathematics in the Natural Sciences. In this episode, Kyle shares a few thoughts along these lines with Linh Da. The discussion winds up with a brief introduction to Zipf\'s law. When applied to natural language, Zipf\'s law states that the frequency of any given word in a corpus (regardless of language) will be proportional to its rank in the frequency table. ... [more]


Semantic search at Github

Github is many things besides source control. It\'s a social network, even though not everyone realizes it. It\'s a vast repository of code. It\'s a ticketing and project management system. And of course, it has search as well. In this episode, Kyle interviews Hamel Husain about his research into semantic code search. ... [more]


Text World and Word Embedding Lower Bounds

In the first half of this episode, Kyle speaks with Marc-Alexandre Côté and Wendy Tay about Text World.  Text World is an engine that simulates text adventure games.  Developers are encouraged to try out their reinforcement learning skills building agents that can programmatically interact with the generated text adventure games.   In the second half of this episode, Kyle interviews Kevin Patel about his paper Towards Lower Bounds on Number of Dimensions for Word Embeddings.  In this research, the explore an important question of how many hidden nodes to use when creating a word embedding. ... [more]


word2vec

Word2vec is an unsupervised machine learning model which is able to capture semantic information from the text it is trained on. The model is based on neural networks. Several large organizations like Google and Facebook have trained word embeddings (the result of word2vec) on large corpora and shared them for others to use. The key algorithmic ideas involved in word2vec is the continuous bag of words model (CBOW). In this episode, Kyle uses excerpts from the 1983 cinematic masterpiece War Games, and challenges Linhda to guess a word Kyle leaves out of the transcript. This is similar to how word2vec is trained. It trains a neural network to predict a hidden word based on the words that appear before and after the missing location. ... [more]


Let\'s Talk About Natural Language Processing

This episode reboots our podcast with the theme of Natural Language Processing for the next few months. We begin with introductions of Yoshi and Linh Da and then get into a broad discussion about natural language processing: what it is, what some of the classic problems are, and just a bit on approaches. Finishing out the show is an interview with Lucy Park about her work on the KoNLPy library for Korean NLP in Python. If you want to share your NLP project, please join our Slack channel.  We\'re eager to see what listeners are working on! http://konlpy.org/en/latest/     ... [more]