Blog

Apr 25, 2018 · newsletter

Simple Architectures Outperform Complex Ones in Language Modeling

Are novel, complex, and specialized neural network architectures always better for language modeling? Recent papers have shown otherwise. Language models are used to predict the next token given the preceeding tokens. Most operate at word-level or character-level. Word-level models have large vocabulary sizes (how many words are there in the English language?) compared to character-level models (there are 26 letters in the English language). This means that character-level models require less memory. On the other hand, when processing a sentence, character-level models see a large number of tokens (each character is a token) compared to word-level models. A large number of tokens (long sequence) is harder for neural networks because of the vanishing gradients problem.

A paper by Salesforce research shows that a properly regulated vanilla recurrent neural network (LSTM or a cheaper counter part QRNN) can achieve state-of-the-art results on both character-level and word-level datasets. The architecture is simple: the model consists of a trainable embedding layer, one or more layers of stacked recurrent neural network, and a softmax classifier. The embedding and softmax classifier layers utilize tied weights, meaning that these two layers share the same weight. To speed up the model (slow because of large vocabulary sizes), a version of adaptive softmax extended to allow for tied weights is used. The network is regularized using DropConnect (generalization of DropOut where the weights, rather than nodes, are set to zero) on the recurrent hidden-to-hidden weight matrices to prevent overfitting on the recurrent connections. This regularization approach does not require any modifications to an RNN’s formulation and allows black box RNN implementations to be used. Black box implementations are preferred because they often run faster due to low-level hardware-specific optimizations.

Relative importance of hyperparameters for word-level task on the smaller WikiText-2 dataset using QRNN (image source)

In addition to achieving state-of-the-art results, experiments with the above model show that QRNN is less successful than LSTM at character-level tasks, even with substantial hyperparameter tuning. QRNN combines the best of convolutional neural network (CNN) and recurrent neural network (RNN). It allows for parallel computation across both timestep and minibatch dimensions (CNN) while retaining sequential information (RNN). In doing so, it uses a simplified hidden-to-hidden transition function which is element-wise rather than full-matrix multiplication. The authors conjecture that this simplified transition function prevents full communication between hidden units in the RNN, making it less suitable for character-level language models. The experiments on QRNN also show that weight dropout is the most important hyperparameter - the number of layers and dimension sizes matters relatively less. We think the paper is interesting because it: i) confirms that novel and complex is not always better; ii) shows character-level and word-level models are not easily transferable; and iii) attempts to rank hyperparameter importance (useful!)

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Latest posts

Nov 15, 2020 · post

Representation Learning 101 for Software Engineers

by Victor Dibia · Figure 1: Overview of representation learning methods. TLDR; Good representations of data (e.g., text, images) are critical for solving many tasks (e.g., search or recommendations). Deep representation learning yields state of the art results when used to create these representations. In this article, we review methods for representation learning and walk through an example using pretrained models. Introduction Deep Neural Networks (DNNs) have become a particularly useful tool in building intelligent systems that simplify cognitive tasks for users.
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Jun 22, 2020 · post

How to Explain HuggingFace BERT for Question Answering NLP Models with TF 2.0

by Victor · Given a question and a passage, the task of Question Answering (QA) focuses on identifying the exact span within the passage that answers the question. Figure 1: In this sample, a BERTbase model gets the answer correct (Achaemenid Persia). Model gradients show that the token “subordinate ..” is impactful in the selection of an answer to the question “Macedonia was under the rule of which country?". This makes sense .. good for BERTbase.
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Jun 16, 2020 · notebook

Evaluating QA: Metrics, Predictions, and the Null Response →

by Melanie · A deep dive into computing QA predictions and when to tell BERT to zip it! In our last post, Building a QA System with BERT on Wikipedia, we used the HuggingFace framework to train BERT on the SQuAD2.0 dataset and built a simple QA system on top of the Wikipedia search engine. This time, we’ll look at how to assess the quality of a BERT-like model for Question Answering.
qa.fastforwardlabs.com
May 19, 2020 · notebook

Building a QA System with BERT on Wikipedia →

by Melanie · So you’ve decided to build a QA system. You want to start with something simple and general so you plan to make it open domain using Wikipedia as a corpus for answering questions. You want to use the best NLP that your compute resources allow (you’re lucky enough to have access to a GPU) so you’re going to focus on the big, flashy Transformer models that are all the rage these days.
qa.fastforwardlabs.com
Apr 28, 2020 · notebook

Intro to Automated Question Answering →

by Melanie · Welcome to the first edition of the Cloudera Fast Forward blog on Natural Language Processing for Question Answering! Throughout this series, we’ll build a Question Answering (QA) system with off-the-shelf algorithms and libraries and blog about our process and what we find along the way. We hope to wind up with a beginning-to-end documentary that provides:
qa.fastforwardlabs.com
Apr 1, 2020 · newsletter

Enterprise Grade ML

by Shioulin · At Cloudera Fast Forward, one of the mechanisms we use to tightly couple machine learning research with application is through application development projects for both internal and external clients. The problems we tackle in these projects are wide ranging and cut across various industries; the end goal is a production system that translates data into business impact. What is Enterprise Grade Machine Learning? Enterprise grade ML, a term mentioned in a paper put forth by Microsoft, refers to ML applications where there is a high level of scrutiny for data handling, model fairness, user privacy, and debuggability.
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Reports

In-depth guides to specific machine learning capabilities

Prototypes

Machine learning prototypes and interactive notebooks
Library

NeuralQA

A usable library for question answering on large datasets.
https://neuralqa.fastforwardlabs.com
Notebook

Explain BERT for Question Answering Models

Tensorflow 2.0 notebook to explain and visualize a HuggingFace BERT for Question Answering model.
https://colab.research.google.com/drive/1tTiOgJ7xvy3sjfiFC9OozbjAX1ho8WN9?usp=sharing
Notebooks

NLP for Question Answering

Ongoing posts and code documenting the process of building a question answering model.
https://qa.fastforwardlabs.com
Notebook

Interpretability Revisited: SHAP and LIME

Explore how to use LIME and SHAP for interpretability.
https://colab.research.google.com/drive/1pjPzsw_uZew-Zcz646JTkRDhF2GkPk0N

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Cloudera Fast Forward is an applied machine learning reseach group.
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