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Amazon senior applied scientist Jack FitzGerald, delivering a keynote talk at the joint Language Intelligence @ Work and SEMANTiCS conference in Vienna, Austria.

Scaling multilingual virtual assistants to 1,000 languages

Self-supervised training, distributed training, and knowledge distillation have delivered remarkable results, but they’re just the tip of the iceberg.

Yesterday at the joint Language Intelligence @ Work and SEMANTiCS conference in Vienna, Austria, Amazon senior applied scientist Jack FitzGerald delivered a keynote talk on multilingual virtual assistants and the path toward a massively multilingual future. This is an edited version of his talk.

The evolution of human-computer interaction paradigms

In the past 50 years, computing technology has progressed from text-based terminal inputs, to graphical user interfaces, to predominantly web-based applications, through the mobile era, and finally into the era of a voice user interface and ambient computing.

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A brief history of computing interfaces.

Each of these paradigms has its own challenges with respect to multilingualism, whether it was the migration from ASCII to Unicode or proper character rendering on a website. However, I would argue that a voice AI system is the most difficult paradigm yet with respect to massive multilingualism.

The first reason is that the input space for voice interface commands is unbounded: the user can phrase each command in hundreds of different ways, all of which are valid. Another reason is that even within a single language, there can be many different dialects and accents.

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Most important, the coupling between language and culture is inescapable. Whether it’s the level of formality used, preferred activities, or religious differences, there isn’t a one-size-fits-all solution. Instead, we must adapt the virtual assistant to understand cultural context and say only things that are appropriate for a given locale.

Voice AI systems today

A typical voice AI system includes automatic-speech-recognition models, which convert raw audio into text; natural-language understanding models, which determine the user’s intent and recognize named entities; a central service for arbitration and dialogue management, which routes commands to the proper services or skills; and finally, a text-to-speech model, which issues the output. Additional tasks might include expansion of the underlying knowledge graph and semantic parsing, localization of touch screen content, or local information services.

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An overview of Alexa’s design.

Let’s look at some of the operational considerations for supporting multiple languages in such models. One is the training data: they must be topically exhaustive, meaning that they cover the full spectrum of possible user utterances, and they must be culturally exhaustive — for instance, covering all of the holidays a user might celebrate. They must also remain up-to-date, and it’s not always easy to add something new to the model without regression on existing functionalities.

A second consideration is in-house testing. Though in many cases one can get away with synthetic or otherwise artificial data for model training, for testing it’s important to have realistic utterances. Those typically need to come from humans, and collecting them can be a major expense. It’s also useful to perform live, interactive testing, which requires people who can speak and understand each language that the system supports.

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Finally, it’s important to have the ability to support users and process their feedback. In most cases, this again requires staff who understand each of the supported languages.

Ultimately, human-based processes are not very scalable if our goal is to support thousands of languages. Instead, we must turn to technology to the greatest extent possible.

Multilingual modeling today

One of the leading reasons for the current success of multilingual text models is self-supervision.

In traditional supervised learning, a model would be trained from scratch on the desired task. If we wanted a model that would classify the sentiment of a product review, for example, we would manually annotate a bunch of product reviews, and we would use that dataset to train the model.

Today, however, we make use of transfer learning, in which text models are pretrained on terabytes of text data that don’t require manual annotation. Instead, the training procedure leverages the structure inherent to the text itself.

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Self-supervised-training objectives.

We’ll call this self-supervised pretraining With the masked-language-modeling training objective, for instance, the model is fed the input “for [MASK] out loud!”, and it must predict that “[MASK]” should be filled with the word “crying”. Other objectives, such as causal language modeling, span filling, deshuffling, and denoising can also be used.

Because the datasets required for self-supervised pretraining are unlabeled and monolingual, we can leverage troves of data, such as Common Crawl web scrapes, every Wikipedia page in existence, thousands of books and news articles, and more. Couple these large datasets with highly parallelizable architectures such as transformers, which can be trained on over a thousand GPUs with near linear scaling, and we can build models with tens or hundreds of billions of dense parameters. Such has been the focus for many people in the field for the past few years, including the Alexa Teacher Model team.

One incredible consequence of the transfer learning paradigm is called zero-shot learning. In the context of multilingual modeling, it works like this: the modeler begins by pretraining the model on some set of languages, using self-supervision. As an example, suppose that the modeler trains a model on English, French, and Japanese using every Wikipedia article in those three languages.

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The next step is to adapt the model to a particular task using labeled data. Suppose that the modeler has a labeled dataset for intent classification, but only in English. The modeler can go ahead and fine-tune the model on the English data, then run it on the remaining languages.

Despite the fact that the model was never trained to do intent classification with French or Japanese data, it can still classify intents in those languages, by leveraging what it learned about those languages during pretraining. Given that the acquisition of labeled data is often a bottleneck, this property of language models is highly valuable for language expansion. Of course, zero-shot learning is just the extreme end of a continuum: transfer learning helps even out performance when the labeled data in different languages is imbalanced.

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Zero-shot learning for multilingual adaptation.

The next step up the data efficiency ladder is performing tasks without any additional training or fine tuning, using only a couple of labeled records or none at all. This is possible through “in-context learning,” which was popularized in the GPT-3 paper.

To perform in-context learning, simply take a pretrained model and feed it the appropriate prompts. Think of a prompt is a hint to the model about the task it should perform. Suppose that we want the model to summarize a passage. We might prefix the passage with the word “Passage” and a colon and follow it with the word “Summary” and a colon. The model would then generate a summary of the passage.

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This is the zero-shot in-context learning case, meaning that no fine-tuning is performed, and no labeled data are needed. To improve task performance, we can feed a few examples to the model before asking it to perform the task. Though this does require some labeled data, the amount is small, usually in the tens of examples only.

Our Alexa Teacher Model team recently trained and tested a 20-billion-parameter sequence-to-sequence model that was multilingual and showed nice performance for in-context learning. For example, we showed state-of-the-art performance on machine translation with in-context learning. The model can achieve competitive BLEU scores even for some low-resource languages, which is incredible given that no parallel data was used during pretraining, and no labeled data besides a single example was used at any step in the process.

We were particularly proud of the relatively small size of this model, which could compete with much larger models because it was trained on more data. (The Chinchilla model from OpenAI showed a similar result.) Though a large model trained on a smaller dataset and a smaller model trained on a larger dataset may use the same total compute at training time, the smaller model will require less compute and memory during inference, which is a key factor in real applications.

Given that models demonstrate multilingual understanding even without labeled data or parallel data, you may be wondering what’s happening inside of the model. Since the days of word2vec and earlier, we’ve represented characters, words, sentences, documents, and other inputs as vectors of floats, also known as embeddings, hidden states, and representations. Concepts cluster in certain areas of the representational space.

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As humans, we can think only in three dimensions, whereas these representations are high-dimensional, but you can visualize this clustering in two or three dimensions as a reductive approximation. All the languages the model supports would cluster the concept of sitting in a chair in one region of the representational space; the concept of the ocean would inhabit a different cluster; and so forth.

Indeed, Pires et al. have shown that synonymous words across languages cluster together in the mBERT model. When examining 5,000 sentence pairs from the WMT16 dataset, they found that, given a sentence and its embedding in one language, the correct translation from another language is the closest embedding to the source embedding up to 75% of the time.

This manner of clustering can also be manipulated by changing the objective function. In their work on speech-to-text-modeling, Adams et al., from Johns Hopkins, were seeing undesirable clustering by language, rather than by phonemes, in the representational space. They were able to correct by adding training objectives around phoneme prediction and language identification.

The Alexa Teacher Model distillation pipeline

Once we have multilingual models, how do we adapt them to a real system? At the recent KDD conference, we presented a paper describing the Alexa Teacher Model pipeline, consisting of the following steps.

First, a multilingual model with billions of parameters is trained on up to a trillion tokens taken from Common Crawl web scrapes, Wikipedia articles, and more. Second, the models are further trained on in-domain, unlabeled data from a real system. Third, the model is distilled into smaller sizes that can be used in production. The final models can then be fine-tuned using labeled data and deployed.

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The Alexa Teacher Model (AlexaTM) pipeline. The Alexa Teacher Model is trained on a large set of GPUs (left), then distilled into smaller variants (center), whose size depends on their uses. The end user adapts a distilled model to its particular use by fine-tuning it on in-domain data (right).

In tests, we found that our model was more accurate than a publicly available pretrained model fine-tuned on labeled data, and it significantly reduced customer dissatisfaction relative to a model trained by a smaller teacher model (85 million parameters, say, instead of billions). In short, we’ve verified that we can leverage the additional learning capacity of large, multilingual models for production systems requiring low latency and low memory consumption.

Scaling to 1,000 languages

I mentioned the fascinating ability of language models to learn joint representations of multiple languages without labeled or parallel data. This ability is crucial for us to scale to many languages. However, as we scale, we need test data that we can trust so that we can evaluate our progress.

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Toward this end, my team at Amazon recently released a new benchmark for multilingual natural-language understanding called MASSIVE, which is composed of one million labeled records spanning 51 languages, 18 domains, 60 intents, and 55 slots. All of the data were created by native speakers of the languages. We also released a GitHub repository with code that can be used as a baseline for creating multilingual NLU models, as well as leaderboards on eval.ai.

Now, you may retort that 51 languages is still a long ways from 1,000 languages. This is true, but we purposefully chose our languages in order to maximize typological diversity while staying within our budget. Our languages span 29 language genera, 14 language families, and 21 distinct scripts or alphabets. The diversity of the chosen languages allows a modeler to test technology that should scale to many more languages within each represented genus, family, and script.

That said, we certainly have some major gaps in language coverage, including across native North and South American languages, African languages, and Australian languages. Yet we are optimistic that our fellow researchers across the field will continue to produce new labeled benchmark datasets for the world’s thousands of low-resource languages.

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The 51 languages of MASSIVE, including scripts and genera.

Another difficulty with our current modeling approaches is that they rely on data sources such as web scrapes, encyclopedic articles, and news articles, which are highly skewed toward a small set of languages. Wang, Ruder, and Neubig recently presented some fascinating work leveraging bilingual lexicons — corpora consisting of word-level translations — to improve language model performance for low-resource languages. Lexicons cover a far greater portion of the world’s languages than our typical data sources for language modeling, making this an exciting approach.

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Researchers, missionaries, and businesspeople have been created fundamental linguistic resources for decades, from Bible translations to the Unimorph corpus. The Unimorph datasets are used for the SIGMORPHON shared task, in which a model must predict the correct formulation of word given that word’s root and certain morphological transformations, such as part of speech, tense, and person. We must find more ways to leverage such resources when creating massively multilingual voice AI systems.

As a final technique for scaling to many more languages, we can consider what we in Alexa call “self-learning.” Some of my Alexa colleagues published a paper showing that we can mine past utterances to improve overall system performance. For example, if a user rephrases a request as part of a multiturn interaction, as shown on the left in the figure below, or if different users provide variations for the same desired goal, as shown on the right, then we can make soft assumptions that the different formulations are synonymous.

All of these cases can be statistically aggregated to form new training sets to update the system, without the need to manually annotate utterances. In a multilingual system, such technology is particularly valuable after the initial launch of a language, both to improve performance generally and to adapt to changes in the lexicon.

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Alexa’s self-learning mechanism.

The road ahead

I hope that you share my wonder at the current state of the art — the scale of language-model training, the magic of zero-shot learning, and the distillation of knowledge into compact models that can run in latency-sensitive systems. All of this is incredible, but we’ve only scratched the surface of supporting the world’s 7,000 languages.

To move into the next era of massive multilingualism, we must build new and increasingly powerful models that can take advantage of low-cost data, particularly unlabeled monolingual data. We must also build models that can leverage existing and upcoming linguistic resources, such as bilingual lexicons and morphological-transformation databases. And finally, we must expand available language resources across more languages and domains, including more unlabeled monolingual corpora, more parallel resources, and more realistic, labeled, task-specific datasets.

Increased multilingualism is a win for all people everywhere. Each language provides a unique perspective on the world in which we live. A rich plurality of perspectives leads to a deeper understanding of our fellow people and of all creation.

Keep building.

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Here at Amazon, we embrace our differences. We are committed to furthering our culture of diversity and inclusion of our teams within the organization. How do you get items to customers quickly, cost-effectively, and—most importantly—safely, in less than an hour? And how do you do it in a way that can scale? Our teams of hundreds of scientists, engineers, aerospace professionals, and futurists have been working hard to do just that! We are delivering to customers, and are excited for what’s to come. Check out more information about Prime Air on the About Amazon blog (https://www.aboutamazon.com/news/transportation/amazon-prime-air-delivery-drone-reveal-photos). If you are seeking an iterative environment where you can drive innovation, apply state-of-the-art technologies to solve real world delivery challenges, and provide benefits to customers, Prime Air is the place for you. Come work on the Amazon Prime Air Team! We are seeking a highly skilled Navigation Scientist to help develop advanced algorithms and software for our Prime Air delivery drone program. In this role, you will conduct comprehensive navigation analysis to support cross-functional decision-making, define system architecture and requirements, contribute to the development of flight algorithms, and actively identify innovative technological opportunities that will drive significant enhancements to meet our customers' evolving demands. Export Control License: This position may require a deemed export control license for compliance with applicable laws and regulations. Placement is contingent on Amazon’s ability to apply for and obtain an export control license on your behalf.
US, WA, Seattle
Do you want to create the greatest-possible worldwide impact in Robotics? Amazon has the world's most exciting treasure trove of robotics challenges. At Amazon Robotics we build high-performance, real-time robotic systems that can perceive, learn, and act intelligently alongside humans—at Amazon scale. Amazon Robotics invents and scales AI systems for robotics in fulfillment. Our mission is to enable robots to interact safely, efficiently, and fluently high density real-world fulfillment centers. Our AI solutions enable robots to learn from their own experiences, from each other, and from humans to build intelligence that feeds itself. We hire and develop collaborative subject matter experts in AI with a focus on computer vision, deep learning, semi-supervised and unsupervised learning. We target high-impact algorithmic unlocks in areas such as scene and activity understanding, large scale generative models, closed-loop control, robotic grasping and manipulation—all of which have high-value impact for our current and future fulfillment networks. We are seeking a hands-on, seasoned Applied Scientists who will be deep in code and algorithms; who are technically strong in building scalable vision systems across item understanding, pose estimation, class imbalanced classifiers, identification and segmentation. As a Applied Scientist, you will contribute to the research and development of advanced robotic systems; your work along with other top-notch scientists and engineers will deliver the world's most scalable and robust robotic systems. You will drive ideas to products using paradigms such as deep learning, semi supervised learning and active learning. As a Applied Scientist, you will also help lead and mentor our team of applied scientists and engineers. You will take on challenging customer problems, distill customer requirements, and then deliver solutions that either leverage existing academic and industrial research or utilize your own out-of-the-box but pragmatic thinking. In addition to coming up with novel solutions and prototypes, you will directly contribute to implementation while you lead. A successful candidate has excellent technical depth, scientific vision, project management skills, great communication skills, and a drive to achieve results in a collaborative team environment. You should enjoy the process of solving real-world problems that, quite frankly, haven’t been solved at scale anywhere before. Along the way, we guarantee you’ll get opportunities to be a fearless disruptor, prolific innovator, and a reputed problem solver—someone who truly enables AI and robotics to significantly impact the lives of millions of consumers. Key job responsibilities Architect, design, and implement Machine Learning models for vision systems on robotic platforms Optimize, deploy, and support at scale ML models on the edge. Influence the team's strategy and contribute to long-term vision and roadmap. Work with stakeholders across , science, and operations teams to iterate on design and implementation. Maintain high standards by participating in reviews, designing for fault tolerance and operational excellence, and creating mechanisms for continuous improvement. Prototype and test concepts or features, both through simulation and emulators and with live robotic equipment Work directly with customers and partners to test prototypes and incorporate feedback Mentor other engineer team members. A day in the life Amazon offers a full range of benefits for you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include: 1. Medical, Dental, and Vision Coverage 2. Maternity and Parental Leave Options 3. Paid Time Off (PTO) 4. 401(k) Plan If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply!
IN, HR, Gurugram
Our customers have immense faith in our ability to deliver packages timely and as expected. A well planned network seamlessly scales to handle millions of package movements a day. It has monitoring mechanisms that detect failures before they even happen (such as predicting network congestion, operations breakdown), and perform proactive corrective actions. When failures do happen, it has inbuilt redundancies to mitigate impact (such as determine other routes or service providers that can handle the extra load), and avoids relying on single points of failure (service provider, node, or arc). Finally, it is cost optimal, so that customers can be passed the benefit from an efficiently set up network. Amazon Shipping is hiring Applied Scientists to help improve our ability to plan and execute package movements. As an Applied Scientist in Amazon Shipping, you will work on multiple challenging machine learning problems spread across a wide spectrum of business problems. You will build ML models to help our transportation cost auditing platforms effectively audit off-manifest (discrepancies between planned and actual shipping cost). You will build models to improve the quality of financial and planning data by accurately predicting ship cost at a package level. Your models will help forecast the packages required to be pick from shipper warehouses to reduce First Mile shipping cost. Using signals from within the transportation network (such as network load, and velocity of movements derived from package scan events) and outside (such as weather signals), you will build models that predict delivery delay for every package. These models will help improve buyer experience by triggering early corrective actions, and generating proactive customer notifications. Your role will require you to demonstrate Think Big and Invent and Simplify, by refining and translating Transportation domain-related business problems into one or more Machine Learning problems. You will use techniques from a wide array of machine learning paradigms, such as supervised, unsupervised, semi-supervised and reinforcement learning. Your model choices will include, but not be limited to, linear/logistic models, tree based models, deep learning models, ensemble models, and Q-learning models. You will use techniques such as LIME and SHAP to make your models interpretable for your customers. You will employ a family of reusable modelling solutions to ensure that your ML solution scales across multiple regions (such as North America, Europe, Asia) and package movement types (such as small parcel movements and truck movements). You will partner with Applied Scientists and Research Scientists from other teams in US and India working on related business domains. Your models are expected to be of production quality, and will be directly used in production services. You will work as part of a diverse data science and engineering team comprising of other Applied Scientists, Software Development Engineers and Business Intelligence Engineers. You will participate in the Amazon ML community by authoring scientific papers and submitting them to Machine Learning conferences. You will mentor Applied Scientists and Software Development Engineers having a strong interest in ML. You will also be called upon to provide ML consultation outside your team for other problem statements. If you are excited by this charter, come join us!
US, WA, Seattle
The AWS Partner Intelligence Science team is hiring an Applied Scientist to develop science products that support AWS initiatives to grow AWS Partners. The team is seeking candidates with strong background in machine learning and engineering, creativity, curiosity, and great business judgment. As an applied scientist on the team, you will work on targeting and lead prioritization related AI/ML products, recommendation systems, and deliver them into the production ecosystem. You are comfortable with ambiguity and have a deep understanding of ML algorithms and an analytical mindset. You are capable of summarizing complex data and models through clear visual and written explanations. You thrive in a collaborative environment and are passionate about learning. Key job responsibilities - Work with customers, product managers, scientists, and engineers to deliver high-quality science products - Experiment with large amounts of data to deliver the best possible science solutions - Design, build, and deploy innovative ML solutions to impact AWS Co-Sell initiatives About the team The AWS Partner Intelligence team is the center of Analytics, Insights, and Science supporting the AWS Specialist Partner Organization on its mission to provide customers with an outstanding experience while working with AWS partners. The Partner Intelligence Science team builds science solutions to boost productivity and accelerate AWS and AWS partners' revenue growth.