DataBloom

Posted by David Ha, Staff Research Scientist and Yujin Tang, Research Software Engineer, Google Research, Tokyo

“The brain is able to use information coming from the skin as if it were coming from the eyes. We don’t see with the eyes or hear with the ears, these are just the receptors, seeing and hearing in fact goes on in the brain.”

– Paul Bach-y-Rita¹

People have the amazing ability to use one sensory modality (e.g., touch) to supply environmental information normally gathered by another sense (e.g., vision). This adaptive ability, called sensory substitution, is a phenomenon well-known to neuroscience. While difficult adaptations — such as adjusting to seeing things upside-down, learning to ride a “backwards” bicycle, or learning to “see” by interpreting visual information emitted from a grid of electrodes placed on one’s tongue — require anywhere from weeks, months or even years to attain mastery, people are able to eventually adjust to sensory substitutions.

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Examples of Sensory Substitution. Left: Tongue Display Unit (Maris and Bach-y-Rita, 2001; Image: Kaczmarek, 2011). Right: “Upside down goggles” initially conceived by Erismann and Kohler in 1931. (Image Wikipedia).

In contrast, most neural networks are not able to adapt to sensory substitutions at all. For instance, most reinforcement learning (RL) agents require their inputs to be in a pre-specified format, or else they will fail. They expect fixed-size inputs and assume that each element of the input carries a precise meaning, such as the pixel intensity at a specified location, or state information, like position or velocity. In popular RL benchmark tasks (e.g., Ant or Cart-pole), an agent trained using current RL algorithms will fail if its sensory inputs are changed or if the agent is fed additional noisy inputs that are unrelated to the task at hand.

In “The Sensory Neuron as a Transformer: Permutation-Invariant Neural Networks for Reinforcement Learning”, a spotlight paper at NeurIPS 2021, we explore permutation invariant neural network agents, which require each of their sensory neurons (receptors that receive sensory inputs from the environment) to figure out the meaning and context of its input signal, rather than explicitly assuming a fixed meaning. Our experiments show that such agents are robust to observations that contain additional redundant or noisy information, and to observations that are corrupt and incomplete.

Permutation invariant reinforcement learning agents adapting to sensory substitutions. Left: The ordering of the ant’s 28 observations are randomly shuffled every 200 time-steps. Unlike the standard policy, our policy is not affected by the suddenly permuted inputs. Right: Cart-pole agent given many redundant noisy inputs (Interactive web-demo).

In addition to adapting to sensory substitutions in state-observation environments (like the ant and cart-pole examples), we show that these agents can also adapt to sensory substitutions in complex visual-observation environments (such as a CarRacing game that uses only pixel observations) and can perform when the stream of input images is constantly being reshuffled:

We partition the visual input from CarRacing into a 2D grid of small patches, and shuffled their ordering. Without any additional training, our agent still performs even when the original training background (left) is replaced with new images (right).

Method
Our approach takes observations from the environment at each time-step and feeds each element of the observation into distinct, but identical neural networks (called “sensory neurons”), each with no fixed relationship with one another. Each sensory neuron integrates over time information from only their particular sensory input channel. Because each sensory neuron receives only a small part of the full picture, they need to self-organize through communication in order for a global coherent behavior to emerge.

Illustration of observation segmentation.We segment each input into elements, which are then fed to independent sensory neurons. For non-vision tasks where the inputs are usually 1D vectors, each element is a scalar. For vision tasks, we crop each input image into non-overlapping patches.

We encourage neurons to communicate with each other by training them to broadcast messages. While receiving information locally, each individual sensory neuron also continually broadcasts an output message at each time-step. These messages are consolidated and combined into an output vector, called the global latent code, using an attention mechanism similar to that applied in the Transformer architecture. A policy network then uses the global latent code to produce the action that the agent will use to interact with the environment. This action is also fed back into each sensory neuron in the next time-step, closing the communication loop.

Overview of the permutation-invariant RL method. We first feed each individual observation (ot) into a particular sensory neuron (along with the agent’s previous action, at-1). Each neuron then produces and broadcasts a message independently, and an attention mechanism summarizes them into a global latent code (mt) that is given to the agent’s downstream policy network (𝜋) to produce the agent’s action at.

Why is this system permutation invariant? Each sensory neuron is an identical neural network that is not confined to only process information from one particular sensory input. In fact, in our setup, the inputs to each sensory neuron are not defined. Instead, each neuron must figure out the meaning of its input signal by paying attention to the inputs received by the other sensory neurons, rather than explicitly assuming a fixed meaning. This encourages the agent to process the entire input as an unordered set, making the system to be permutation invariant to its input. Furthermore, in principle, the agent can use as many sensory neurons as required, thus enabling it to process observations of arbitrary length. Both of these properties will help the agent adapt to sensory substitutions.

Results
We demonstrate the robustness and flexibility of this approach in simpler, state-observation environments, where the observations the agent receives as inputs are low-dimensional vectors holding information about the agent’s states, such as the position or velocity of its components. The agent in the popular Ant locomotion task has a total of 28 inputs with information that includes positions and velocities. We shuffle the order of the input vector several times during a trial and show that the agent is rapidly able to adapt and is still able to walk forward.

In cart-pole, the agent’s goal is to swing up a cart-pole mounted at the center of the cart and balance it upright. Normally the agent sees only five inputs, but we modify the cartpole environment to provide 15 shuffled input signals, 10 of which are pure noise, and the remainder of which are the actual observations from the environment. The agent is still able to perform the task, demonstrating the system’s capacity to work with a large number of inputs and attend only to channels it deems useful. Such flexibility may find useful applications for processing a large unspecified number of signals, most of which are noise, from ill-defined systems.

We also apply this approach to high-dimensional vision-based environments where the observation is a stream of pixel images. Here, we investigate screen-shuffled versions of vision-based RL environments, where each observation frame is divided into a grid of patches, and like a puzzle, the agent must process the patches in a shuffled order to determine a course of action to take. To demonstrate our approach on vision-based tasks, we created a shuffled version of Atari Pong.

Shuffled Pong results. Left: Pong agent trained to play using only 30% of the patches matches performance of Atari opponent. Right: Without extra training, when we give the agent more puzzle pieces, its performance increases.

Here the agent’s input is a variable-length list of patches, so unlike typical RL agents, the agent only gets to “see” a subset of patches from the screen. In the puzzle pong experiment, we pass to the agent a random sample of patches across the screen, which are then fixed through the remainder of the game. We find that we can discard 70% of the patches (at these fixed-random locations) and still train the agent to perform well against the built-in Atari opponent. Interestingly, if we then reveal additional information to the agent (e.g., allowing it access to more image patches), its performance increases, even without additional training. When the agent receives all the patches, in shuffled order, it wins 100% of the time, achieving the same result with agents that are trained while seeing the entire screen.

We find that imposing additional difficulty during training by using unordered observations has additional benefits, such as improving generalization to unseen variations of the task, like when the background of the CarRacing training environment is replaced with a novel image.

Shuffled CarRacing results. The agent has learned to focus its attention (indicated by the highlighted patches) on the road boundaries. Left: Training environment. Right: Test environment with new background.

Conclusion
The permutation invariant neural network agents presented here can handle ill-defined, varying observation spaces. Our agents are robust to observations that contain redundant or noisy information, or observations that are corrupt and incomplete. We believe that permutation invariant systems open up numerous possibilities in reinforcement learning.

If you’re interested to learn more about this work, we invite readers to read our interactive article (pdf version) or watch our video. We also released code to reproduce our experiments.

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¹Quoted in Livewired, by David Eagleman.  ^↩

For a while, it may have seemed that Keras for R was in some undecidable state, like Schrödinger’s cat before inspection. It is high time to correct that impression. Keras for R is back, with two recent releases adding powerful capabilities that considerably lighten previously tedious tasks. This post provides a high-level overview. Future posts will go into more detail on some of the most helpful new features, as well as dive into the powerful low-level enhancements that make the former possible.

Posted by Sian Gooding, Intern, Google Research

Illiteracy affects at least 773 million people globally, both young and old. For these individuals, reading information from unfamiliar sources or on unfamiliar topics can be extremely difficult. Unfortunately, these inequalities have been further magnified by the global pandemic as a result of unequal access to education in reading and writing. In fact, UNESCO reports that over 100 million children are falling behind the minimum proficiency level in reading due to COVID-related school closures.

With increasing world-wide access to technology, reading on a device, such as a tablet or phone, has largely taken the place of traditional formats. This provides a unique opportunity to observe reading interactions, e.g., how a reader scrolls through a text, which can inform our understanding of what can make text difficult to read. This understanding is crucial when designing educational applications for low-proficiency readers and language learners, because it can be used to match learners with appropriately leveled texts as well as to support readers in understanding texts beyond their reading level.

In “Predicting Text Readability from Scrolling Interactions”, presented at CoNLL 2021, we show that data from on-device reading interactions can be used to predict how readable a text is. This novel approach provides insights into subjective readability — whether an individual reader has found a text accessible — and demonstrates that existing readability models can be improved by including feedback from scroll-based reading interactions. In order to encourage research in this area and to help enable more personalized tools for language learning and text simplification, we are releasing the dataset of reading interactions generated from our scrolling behavior–based readability assessment of English-language texts.

Understanding Text Difficulty
There are multiple aspects of a text that impact how difficult it is to read, including the vocabulary level, the syntactic structure, and overall coherence. Traditional machine learning approaches to measure readability have exclusively relied on such linguistic features. However, using these features alone does not work well for online content, because such content often contains abbreviations, emojis, broken text, and short passages, which detrimentally impact the performance of readability models.

To address this, we investigated whether aggregate data about the reading interactions of a group can be used to predict how difficult a text is, as well as how reading interactions may differ based on a readers’ understanding. When reading on a device, readers typically interact with text by scrolling in a vertical fashion, which we hypothesize can be used as a coarse proxy for reading comprehension. With this in mind, we recruited 518 paid participants and asked them to read English-language texts of different difficulty levels. We recorded the reading interactions by measuring different features of the participants’ scrolling behavior, such as the speed, acceleration and number of times areas of text were revisited. We then used this information to produce a set of features for a readability classifier.

Predicting Text Difficulty from Scrolling Behavior
We investigated which types of scrolling behaviors were most impacted by text difficulty and tested the significance using linear mixed effect models. In our set up, we have repeated measures, as multiple participants read the same texts and each participant reads more than one text. Using linear mixed-effect models gives us a higher confidence that the differences in interactions we are observing are because of the text difficulty, and not other random effects.

Our results showed that multiple reading behaviors differed significantly based on the text level, for example, the average, maximum and minimum acceleration of scrolling. We found the most significant features to be the total read time and the maximum reading speeds.

We then used these features as inputs to a machine learning algorithm. We designed and trained a support vector machine (i.e., a binary classifier) to predict whether a text is either advanced or elementary based only on scrolling behaviors as individuals interacted with it. The dataset on which the model was trained contains 60 articles, each of which were read by an average of 17 participants. From these interactions we produced aggregate features by taking the mean of the significant measures across participants.

 

We measured the accuracy of the approach using a metric called f-score, which measures how accurate the model is at classifying a text as either “easy” or “difficult” (where 1.0 reflects perfect classification accuracy). We are able to achieve an f-score of 0.77 on this task, using interaction features alone. This is the first work to show that it is possible to predict the readability of a text using only interaction features.

Improving Readability Models
In order to demonstrate the value of applying readability measures from scrolling behaviors to existing readability models, we integrated scroll-based features into the state-of-the-art automated readability assessment tool, which was released as part of the OneStopEnglish corpus. We found that the addition of interaction features improves the f-score of this model from 0.84 to 0.88. In addition, we were able to significantly outperform this system by using interaction information with simple vocabulary features, such as the number of words in the text, achieving an impressive f-score of 0.96.

In our study, we recorded comprehension scores to evaluate the understanding and readability of text for individuals. Participants were asked three questions per article to assess the reader’s understanding of what they had read. The interaction features of an individual’s scrolling behavior was represented as a high dimensional vector. To explore this data, we visualized the reading interaction features for each participant using t-distributed stochastic neighbor embeddings, which is a statistical method for visualizing high-dimensional data. The results revealed clusters in the comprehension score based on how well individuals understood the text. This shows that there is implicit information in reading interactions about the likelihood that an individual has understood a given text. We refer to this phenomenon as subjective readability. This information can be very useful for educational applications or for simplifying online content.

Plot showing t-SNE projection of scroll interactions in 2-dimensions. The color of each data point corresponds to the comprehension score. Clusters of comprehension scores indicate that there are correlations between reading behaviors and comprehension.

Finally, we investigated the extent to which reading interactions vary across audiences. We compared the average scrolling speed across different reader groups, covering reading proficiency and the reader’s first language. We found that the speed distribution varies depending on the proficiency and first language of the audience. This supports the case that first language and proficiency alter the reading behaviors of audiences, which allows us to contextualize the reading behavior of groups and better understand which areas of text may be harder for them to read.

Histogram showing the average speeds of scrolling (in vertical pixels per millisecond) across readers of different proficiency levels (beginner, intermediate and advanced), with lines showing the smoothed trend for each group. A higher average scroll speed indicates faster reading times. For example, a more challenging text that corresponds to slower scroll speeds by advanced readers is associated with higher scroll speeds by beginners because they engage with the text only superficially.

Histogram showing the average speeds of scrolling (in vertical pixels per millisecond) across audiences by first language of the readers, Tamil or English, with lines showing the smoothed trend for each group. A higher average scroll speed indicates faster reading times. Dark blue bars are where the histograms overlap.

Conclusion
This work is the first to show that reading interactions, such as scrolling behavior, can be used to predict the readability of text, which can yield numerous benefits. Such measures are language agnostic, unobtrusive, and robust to noisy text. Implicit user feedback allows insight into readability at an individual level, thereby allowing for a more inclusive and personalisable assessment of text difficulty. Furthermore, being able to judge the subjective readability of text benefits language learning and educational apps. We conducted a 518 participant study to investigate the impact of text readability on reading interactions and are releasing a novel dataset of the associated reading interactions. We confirm that there are statistically significant differences in the way that readers interact with advanced and elementary texts, and that the comprehension scores of individuals correlate with specific measures of scrolling interaction. For more information our conference presentation is available to view.

Acknowledgements
We thank our collaborators Yevgeni Berzak, Tony Mak and Matt Sharifi, as well as Dmitry Lagun and Blaise Aguera y Arcas for their helpful feedback on the paper.