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Rodolfo Guluarte Hale [email protected] Technical Profile Seasoned Full-Stack Engineer with 10+ years of experience building scalable web applications. Expert in modern JavaScript/TypeScript ecosystems (React, Vue.js, Next.js) and cloud-native architectures. Combines leadership in complex projects with hands-on expertise in AI/ML integration, performance optimization, and full-stack development. Main Technologies JavaScript/TypeScript : 10+ years React/Vue.js : 7+ years Node.js/PHP : 8+ years AWS/Cloud : 6+ years Technical Toolkit Frontend : React, Vue.js, Next.js, Redux, Bootstrap Backend : Node.js, Laravel, Express, REST/GraphQL, Microservices AI/ML : OpenAI, LLM Fine-Tuning, NLP Applications Databases : PostgreSQL, MySQL, DynamoDB, Redis, MongoDB DevOps : AWS, Docker, Terraform, CI/CD, GitHub Actions Testing : Cypress, Jest, Playwright Tools : Stripe, Auth0, Twilio, Supabase Experience Machine Learning Engineer | Scale AI, Remote July 2024 – Present Specializing in training and fine-tuning large language models (LLMs) for code generation and analysis. Sr Full-stack Engineer - Contract | xVentures, Remote (New York) January 2024 – July 2024 Led development efforts on a complex web-based platform, focusing on feature enhancements and performance optimization. Key Contributions: Implemented critical features to enhance platform functionality and user experience Conducted comprehensive code reviews and testing, ensuring high-quality deliverables Collaborated with cross-functional teams to streamline design, development, and deployment processes Optimized production environment configurations, improving overall system performance Skills: TypeScript, Supabase, PostgreSQL, Next.JS Full-stack Developer - UpWork | Freelance, Remote February 2023 - January 2024 Developed various projects for clients, showcasing versatility in technology stack and problem-solving. Notable Projects: Project: AI Chat Application Architected and developed a scalable AI chat application using Laravel, Vue.js, and Redis Implemented real-time communication features and robust error handling mechanisms Designed and integrated chat history management system Project: Automated Article Generation and Publishing App Developed a Next.js application integrating Notion databases, OpenAI for content generation, and WordPress for publishing Implemented custom API integrations and automated workflows for content creation and distribution Project: PETA’s VeganStart App Upgrade Enhanced existing mobile application with advanced search algorithms and user-contributed product features Ensured seamless integration of new features within the existing app architecture Sr Full-stack Engineer | Renters Legal Liability LLC, Remote (Salt Lake City, Utah) January 2018 – February 2023 Led the development of a cross-platform insurance application for quote generation and claims management. Key Contributions: Architected and developed a hybrid app using React, React Native, and Node.js Implemented secure payment processing and claims management features Spearheaded migration from JavaScript to TypeScript and integration of Auth0 Single Sign-On Developed custom module for integrating Thought Industries learning platform Full-stack Engineer - Contract | Unbooked Appointments Inc, Remote (Charleston, SC) January 2017 – January 2018 Enhanced a complex scheduling platform with advanced booking functionalities and third-party integrations. Key Achievements: Developed multi-service booking system with vendor availability confirmation Integrated platform with third-party services like Mindbody using REST APIs Optimized calendar functionality for cross-time zone operations Resolved performance bottlenecks using telemetry and profiling tools Software Engineer | xRM.com, Remote (Las Vegas, NV) May 2015 – January 2017 Focused on web application development and CRM customization projects. Notable Projects: Developed web application for LAUSD using ASP.NET MVC 5 Implemented custom workflows and plugins for MS Dynamics CRM Created reusable resource scheduling calendar for MS Dynamics 365 Developed Java connector for MS Dynamics CRM integration Implemented a CRM solution for synchronizing Shopify and BigCommerce with MS Dynamics Software Engineer | Tedesi Sistemas S. de R.L. de C.V, La Paz, México January 2012 – May 2015 Maintained and enhanced point of sale systems and web development projects. Technical Contributions: Maintained POS systems using VB.NET, C#, and MySQL Designed and optimized MySQL databases, including stored procedures and user-defined functions Developed custom web portal using PHP, MySQL, and Silex framework Freelance Developer | La Paz, México July 2006 - December 2011 Provided comprehensive web development and server administration services. Developed and implemented web scraping solutions for various clients using Python. Key Responsibilities: Developed and maintained websites using PHP and MySQL Administered LAMP stack servers Created custom WordPress plugins and themes Education Bachelor in Software Engineering , Universidad Abierta y a Distancia de México (UnAD), Mexican Professional License #: 10145135 Languages Spanish (Native), English (Fluent), French (Basic competence)

As the primary developer for the Bajapack Clients Area, I used PHP to create a robust web-based application that simplifies the shipment and invoice management process for users. This application offers a range of features that enable users to track their active shipments, access shipment history, view tracking numbers, and manage their billing information with ease. Moreover, the ability to add a custom address further streamlines the shipping process, providing an efficient and hassle-free experience. The Bajapack Clients Area is designed with security and reliability in mind, ensuring that all data is securely stored and easily accessible for users.

ISSP is an online platform that provides comprehensive support services for students in Los Angeles Unified School District (LAUSD). It aims to improve student outcomes by addressing their academic, social-emotional, health, family, community, career, and college readiness needs. I was the main developer of ISSP from concept to launch. I worked closely with LAUSD staff and stakeholders throughout the development process. I used agile methodology and user-centered design principles to ensure quality and usability. Some of my responsibilities included: Designing and developing the front-end and back-end functionalities using ASP.NET MVC, C#, SQL Server, JavaScript, React Integrating various APIs such as Microsoft Exchange, Microsoft Implementing security features such as encryption, digital signature,multi-factor authentication, single sign-on Conducting user research, user testing, user feedback, user training, user support Creating documentation, wireframes, mockups, demos, presentations, reports The outcome of ISSP was impressive: It served over 600000 students across more than 1000 schools in LAUSD It increased student engagement, attendance, performance, satisfaction, and retention rates It reduced dropout rates, disciplinary issues, and achievement gaps Integrated Safe School Plan Preview

Refactored the calendar system to be timezone compatible. Implemented the synchronization between Unbooked and Bizlink, Mindbody. Decoupled the system to use a dependency injection container Worked with other developer rewriting the back-end to use a monolithic architecture based on inversion of control. Added features to book multiple services and check availability on the third party vendors. Reduced server load by 90% through telemetry, profiling and rewriting slow methods. Worked on adding the autopilot feature, a feature that sets the ideal price for a service at a given time based on historical performance allowing providers to maximize their profit.

Renters Compliance Portal (RCP), is a web-based application that streamlines the process of providing and verifying renters insurance for property managers and residents alike. With RCP, residents can conveniently upload their insurance proof online, or through phone or fax. The application then automatically verifies the policy’s compliance with the apartment community’s requirements. Moreover, RCP notifies property managers of any changes to the policy, including expiration or cancellation, and provides an option to enroll the unit in the Renters Legal Liability Program (RLL) to safeguard the asset from damage. Ultimately, RCP aims to simplify the burden of managing renters insurance for both parties and elevate their overall living experience.

During a recent project, I had the exciting opportunity to utilize Next.js, JavaScript, and machine learning in order to create custom game avatars. Leveraging Next.js, I developed a server-side rendered React application which allowed users to easily generate and customize their own avatars. JavaScript played an integral role in processing user input and generating the final avatars. To take the project to the next level and make the avatars even more personalized, I integrated machine learning algorithms. These sophisticated algorithms ensured that each avatar was entirely unique and reflected the individual preferences and traits of the user. Overall, this project proved to be a fun and engaging way to explore the intersection of web development and artificial intelligence.

Problem You are given the heads of two sorted linked lists list1 and list2 . Merge the two lists in a one sorted list. The list should be made by splicing together the nodes of the first two lists. Return the head of the merged linked list. Example 1: Input: list1 = [1,2,4], list2 = [1,3,4] Output: [1,1,2,3,4,4] Example 2: Input: list1 = [], list2 = [] Output: [] Example 3: Input: list1 = [], list2 = [0] Output: [0] Constraints: The number of nodes in both lists is in the range [0, 50]. -100

Problem You are given the heads of two sorted linked lists list1 and list2 . Merge the two lists in a one sorted list. The list should be made by splicing together the nodes of the first two lists. Return the head of the merged linked list. Example 1: Input: list1 = [1,2,4], list2 = [1,3,4] Output: [1,1,2,3,4,4] Example 2: Input: list1 = [], list2 = [] Output: [] Example 3: Input: list1 = [], list2 = [0] Output: [0] Constraints: The number of nodes in both lists is in the range [0, 50]. -100 <= Node.val <= 100 Both list1 and list2 are sorted in non-decreasing order. Solution var mergeTwoLists = function ( list1 , list2 ) { // if either list is null we just // return the other if ( ! list1 ) return list2 if ( ! list2 ) return list1 let sorted = new ListNode () const first = sorted let l1pointer = list1 let l2pointer = list2 // loop while there is an element // in either list while ( l1pointer || l2pointer ) { // transverse list1 and keep moving to the next // position while the current value is less than // the next value in the other list while ( l1pointer && ( ! l2pointer || l1pointer . val <= l2pointer . val )) { // we found the node that comes next // move our pointers to the next position sorted . next = l1pointer sorted = sorted . next l1pointer = l1pointer . next } // same as previous loop while ( l2pointer && ( ! l1pointer || l2pointer . val < l1pointer . val ) ) { sorted . next = l2pointer sorted = sorted . next l2pointer = l2pointer . next } } // return the first real node return first . next }

You are given an array prices where prices[i] is the price of a given stock on the ith day. You want to maximize your profit by choosing a single day to buy one stock and choosing a different day in the future to sell that stock. Return the maximum profit you can achieve from this transaction. If you cannot achieve any profit, return 0. Example 1: Input: prices = [7,1,5,3,6,4] Output: 5 Explanation: Buy on day 2 (price = 1) and sell on day 5 (price = 6), profit = 6-1 = 5. Note that buying on day 2 and selling on day 1 is not allowed because you must buy before you sell. Example 2: Input: prices = [7,6,4,3,1] Output: 0 Explanation: In this case, no transactions are done and the max profit = 0. Constraints: 1

You are given an array prices where prices[i] is the price of a given stock on the ith day. You want to maximize your profit by choosing a single day to buy one stock and choosing a different day in the future to sell that stock. Return the maximum profit you can achieve from this transaction. If you cannot achieve any profit, return 0. Example 1: Input: prices = [7,1,5,3,6,4] Output: 5 Explanation: Buy on day 2 (price = 1) and sell on day 5 (price = 6), profit = 6-1 = 5. Note that buying on day 2 and selling on day 1 is not allowed because you must buy before you sell. Example 2: Input: prices = [7,6,4,3,1] Output: 0 Explanation: In this case, no transactions are done and the max profit = 0. Constraints: 1 <= prices.length <= 105 0 <= prices[i] <= 104 Solution This problem is an ideal candidate for dynamic programming , which is keeping the best result until we see a better one. In this case, we start from the end to the beginning, keeping track of the highest return and the highest stock price. /** * @param {number[]} prices * @return {number} */ var maxProfit = function ( prices ) { // dp from end to start if ( ! prices || prices . length < 1 ) return 0 let maxProfit = 0 let high = 0 for ( let i = prices . length ; i >= 0 ; i -- ) { let profit = high - prices [ i ] maxProfit = Math . max ( maxProfit , profit ) high = Math . max ( prices [ i ], high ) } return maxProfit } Source: https://leetcode.com/problems/best-time-to-buy-and-sell-stock/

Solution For this problem we just solve the base problem for a tree with children, and then we use recursion to bubble up the changes. /** * Definition for a binary tree node. * function TreeNode(val, left, right) { * this.val = (val===undefined ? 0 : val) * this.left = (left===undefined ? null : left) * this.right = (right===undefined ? null : right) * } */ /** * @param {TreeNode} root * @return {TreeNode} */ var invertTree = function ( root ) { if ( ! root ) return null const right = root . right const left = root . left root . left = invertTree ( right ) root . right = invertTree ( left ) return root }

Hey there! I’m a seasoned Full Stack Developer with a solid grounding in JavaScript, Node.js, React, and React Native. For over a decade, I’ve been crafting diverse websites and apps for various sectors, including e-commerce, B2B, data synchronization, and educational tools. Back-end wise, I’m skilled in JavaScript, C#, PHP, and familiar with several frameworks. On the front-end, I handle CSS, SASS, JavaScript, and React with ease. I’m also adept at mobile development for both Android and iOS, using native and hybrid technologies. I’m all about continuous learning, keeping up with the latest trends and best practices in web development. My goal is to deliver top-notch, secure, and scalable solutions. I hold a Bachelor’s in Software Engineering from Universidad Nacional Abierta y a Distancia de Mexico and have augmented my skills with relevant courses and certifications. Looking for a friendly and skilled developer to bring your web ideas to life? I’m your guy. Feel free to peruse my past projects and client feedback on my profile. Got a project in mind? Drop me a message or invite me to your job post. I’ll respond pronto. Skills: Design Patterns, SOLID principles JavaScript, TypeScript, PHP, C# React, React Native, NextJS, Svelte Node.js, Remix, Next.js, Nuxt.js, express.js, GraphQL CI/CD, git, docker PostgreSQL, MySQL, MongoDB, Redis, RabbitMQ Cypress, Jest, Mocha Zoho CRM, MS Dynamics CRM Solidity, Ethereum, dApps, blockchain AWS, GCP, Azure, Firebase Recently, I’ve been diving into Rust, cloud computing, and containerization, setting up a home server with Proxmox, Portainer, and Docker. I’m always eager to grasp new tools and technologies. I’m also exploring deep learning and machine learning, focusing on fine-tuning Large Language Models and Stable diffusion. I’m enthusiastic about using these technologies for innovative, impactful solutions. Feel free to contact Me

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Together with my son, we built a Pokemon-inspired NFT game using Solidity, Ethereum, React, JavaScript, and Web3 technologies. The game included a ranking system and a betting system where NFTs and cryptocurrencies could be staked for multiplayer matches. At the beginning of the game, players were given 7 tokens to capture any NFTs in the area. More tokens could be bought to catch more NFTs. When players faced off against each other, they had the option to wager a certain amount of cryptocurrency, NFTs, or both. The winner took home the bet and gained experience. Players didn’t need to worry about their NFT’s health because it was regenerated after each match. The amount of experience gained determined a player’s rank. As players advanced in rank, they unlocked new parts of the map and could capture more powerful NFTs. If a player dropped in rank, previously unlocked areas became locked again. We completed this project as part of Buildspace Nights and Weekends.

I was lucky enough to be selected as a candidate for Career Hunt 2018, a recruitment campaign launched by Work in Estonia in collaboration with some of the biggest IT companies, including Skype, Taxify, Axinom, Finestmedia, Global Gaming Group, Helmes, Nortal, Proekspert, Swedbank, Topia, Twilio, and Veriff. The aim of the campaign was to find talented specialists from around the world for senior IT positions in Estonia. The five-day tech-tour was all-inclusive, and we were flown to Tallinn to meet with recruiters and representatives from the participating companies. We had VIP shortcuts to the interview process and were able to showcase our skills and experience to some of the most influential companies in the IT industry. I had a great time getting to know my fellow candidates at the welcome dinner, which was the first event of the week. We were able to network and make connections with like-minded individuals who shared our excitement for working in Estonia. Over the next few days, we visited some of the most innovative and dynamic IT companies in Estonia, including Skype, Taxify, and Helmes. It was fascinating to see how these companies were using cutting-edge tools and techniques to solve complex problems and shape the future of technology. The interview process was both exciting and nerve-wracking, but the VIP shortcuts allowed us to skip the long waiting times and go straight to the interview room. It was an excellent opportunity to showcase our abilities and experience to some of the most influential companies in the industry. Aside from the interviews, we also had the chance to explore Tallinn and experience the local culture. We visited the old town, which is a UNESCO World Heritage site, and learned about the city’s history and traditions. We also had a chance to sample the local cuisine and try some of the famous Estonian delicacies. Overall, my experience as a selected candidate in Career Hunt 2018 was amazing. I met some of the brightest minds in the IT industry and explored a new country at the same time. I highly recommend Career Hunt to anyone who is looking for a new challenge in their career and wants to explore the exciting opportunities that Estonia has to offer.

ChatGPT, DALL-E y GPT-4 han causado furor en Internet. A pesar de que la investigación en inteligencia artificial ha existido durante 80 años, los últimos desarrollos han despertado la curiosidad de millones dentro y fuera de la industria tecnológica, convirtiendo finalmente a la IA en algo más común. Este hype también ha desatado una carrera de la IA en la industria. Grandes compañías están compitiendo para lanzar los mejores modelos. Las startups están luchando por construir los mejores productos sobre estos modelos: una de cada cinco empresas en la actual cohorte de Y-Combinator está construyendo en el espacio de la IA generativa. Si estás interesado en tener una comprensión más profunda de los últimos desarrollos de machine learning en tu feed de noticias o estás interesado en entender cómo funcionan estos modelos, este post es para ti. Este artículo tiene como objetivo establecer una intuición sobre los términos clave del machine learning para desmitificar algunos de los procesos internos de estos desarrollos. Armado con esta información, abordaremos cómo se entrenan los modelos de lenguaje y cómo funciona la arquitectura de transformadores en publicaciones separadas. Explicando embeddings ¿Alguna vez te has preguntado cómo las computadoras entienden conceptos que los humanos percibimos con nuestros sentidos, como el lenguaje y las imágenes? Por ejemplo, los usuarios envían texto a ChatGPT y este genera una respuesta de texto. ¿Cómo entiende ChatGPT lo que está diciendo el usuario? A través de los embeddings. Las computadoras no entienden el inglés; entienden números, vectores y matrices. Los embeddings son una forma de asignar conceptos a números significativos. “Significativo” en el sentido de que los conceptos similares están físicamente cerca entre sí en un gráfico. Por ejemplo, si quisieras construir un modelo de recomendación de películas, necesitarías una forma de asignar embeddings a las películas. Un embedding potencial podría considerar dónde se encuentran las películas en un gráfico definido por la amistad infantil y el humor. Voilá, la película “The Hangover” ahora puede ser representada de manera significativa por números. Basándose en esta representación, si has visto “The Hangover”, el sistema de recomendación es más probable que te recomiende “The Incredibles” que “Soul”. Es una recomendación bastante mala, pero hey, es un comienzo. Para representar adecuadamente una película, se necesitarían representar muchas más características, lo que resultaría en un vector de embedding más largo. En GloVe, un paquete en línea con embeddings para palabras en inglés utilizadas en Wikipedia, el embedding para la palabra “king” es un vector de 50 dígitos. Los modelos de aprendizaje automático son programas de computadora que encuentran patrones o toman decisiones sobre datos que no han visto antes. Para entrenar un modelo, se buscan valores de parámetros que resulten en la mayor precisión en datos no vistos antes. Después de entrenado, el modelo debe ser probado en datos nunca antes vistos para ver qué tan preciso es. Si la precisión es lo suficientemente alta, el modelo está listo para ser utilizado. Si no, debe ser reentrenado. Relacionando modelos, entrenamiento, pruebas, parámetros y tokens Los parámetros son números que codifican lo que el modelo aprendió de los datos de entrenamiento. El tamaño del modelo es el número de parámetros que tiene y determina cuánto espacio ocupa. El tamaño del modelo no siempre se relaciona con la cantidad de datos de entrenamiento y su aumento no siempre conduce a mejores resultados. Por lo tanto, encontrar el tamaño adecuado es un arte. La tokenización corta los datos de entrada en partes significativas. Al tratar con imágenes, la tokenización implica dividir la imagen en parches. Al tratar con lenguaje, la tokenización implica dividir el texto en palabras/frases frecuentes. En resumen, un modelo = incorporado(entrada) * parámetros = salida. Los parámetros son números que codifican lo que el modelo aprendió de los datos de entrenamiento y el tamaño del modelo es el número de parámetros que tiene y determina cuánto espacio ocupa. La tokenización corta los datos de entrada en partes significativas. LMs, LLMs, NLP, Redes Neuronales El aprendizaje automático (Machine Learning) es una rama de la inteligencia artificial (IA) que se enfoca en enseñar a las computadoras a aprender a partir de los datos sin ser explícitamente programadas. En este artículo, nos enfocaremos en una rama específica de la IA que está muy de moda en estos días: el procesamiento de lenguaje natural (NLP). Modelos de lenguaje Un tipo específico de modelo en NLP son los modelos de lenguaje (LM), un ejemplo notable es GPT-3. Estos modelos están diseñados para predecir la siguiente palabra dada una parte de una oración. Cuando una computadora predice la siguiente palabra de manera precisa una y otra vez, termina generando contenido significativo, como por ejemplo, los posts escritos por ChatGPT. Modelos de Lenguaje de Gran Escala Los Modelos de Lenguaje de Gran Escala (LLM) son solo modelos que predicen la siguiente palabra y tienen una gran cantidad de parámetros (en la escala de cientos de millones). Algunos de los modelos de LLM más famosos son GPT, BERT y T-5. Redes neuronales Una estructura de modelo frecuentemente utilizada en los modelos de lenguaje es la de redes neuronales. Están diseñadas para imitar vagamente cómo funcionan las neuronas en nuestro cerebro. Conclusión El procesamiento de lenguaje natural es una rama importante de la IA que se enfoca en dar a las computadoras la habilidad de entender el lenguaje escrito y hablado de la misma forma en que lo hacen los humanos. Los modelos de lenguaje y las redes neuronales son dos elementos clave en esta rama y han llevado a avances significativos en el campo.

Transformers son la clave del éxito de sistemas como Chat-GPT, DALL-E y otros basados en GPT. Los transformers son la “T” de GPT-4 (Transformers Generativos Preentrenados v4) y su desarrollo ha permitido a las máquinas entender el lenguaje con un nivel de precisión sin precedentes. Para entender la arquitectura de transformers, primero hay que comprender las limitaciones de sus predecesores, las redes neuronales recurrentes (RNN) y las redes de memoria a corto y largo plazo (LSTM). Estos métodos procesan cada palabra de forma secuencial, lo que hace que la capacitación tome mucho tiempo y no se pueda paralelizar, lo que afecta la comprensión del lenguaje. Los transformers resuelven estos problemas al permitir el procesamiento paralelo y el almacenamiento de la información contextual de cada palabra en lugar de depender del procesamiento secuencial. Esto mejora la comprensión del lenguaje y los resultados en la capacitación. La arquitectura de transformers codifica el contexto de una oración en la codificación de cada palabra, lo que permite que cada palabra se procese de forma aislada. De esta manera, el proceso de entrenamiento puede paralelizarse, lo que permite la capacitación en grandes cantidades de datos y mejora drásticamente los resultados. En resumen, los transformers son la clave del éxito de GPT-4, permitiendo a las máquinas entender el lenguaje con un nivel sin precedentes de precisión y comprensión.

In Rust, the concept of lifetimes ensures that all borrows are valid, preventing references from becoming invalid or pointing to memory that no longer exists. When we define a struct that holds references, we need to specify the lifetime of the reference using lifetime annotations. Lifetime annotations describe the relationships between the lifetimes of multiple references to ensure that references remain valid for the duration they are used. When a struct holds a reference, it must declare a lifetime parameter, which is the scope for which that reference is valid. To declare a lifetime parameter for a struct, we use angle brackets and specify the lifetime parameter name. We can then use this lifetime parameter in the struct definition to specify the lifetime of the reference held by the struct. For example, consider the ImportantExcerpt struct defined as: struct ImportantExcerpt

In Rust, the concept of lifetimes ensures that all borrows are valid, preventing references from becoming invalid or pointing to memory that no longer exists. When we define a struct that holds references, we need to specify the lifetime of the reference using lifetime annotations. Lifetime annotations describe the relationships between the lifetimes of multiple references to ensure that references remain valid for the duration they are used. When a struct holds a reference, it must declare a lifetime parameter, which is the scope for which that reference is valid. To declare a lifetime parameter for a struct, we use angle brackets and specify the lifetime parameter name. We can then use this lifetime parameter in the struct definition to specify the lifetime of the reference held by the struct. For example, consider the ImportantExcerpt struct defined as: struct ImportantExcerpt <' a > { part : & ' a str , } Here, the struct ImportantExcerpt has a single field, part, which is a reference to a string slice. The lifetime parameter ‘a is used to specify that the lifetime of the reference part cannot outlive the lifetime of the struct instance. This annotation means that an instance of ImportantExcerpt can’t outlive the reference it holds in its part field. When creating an instance of this struct, we need to ensure that the reference passed as an argument to part lives at least as long as the struct instance itself. Otherwise, we would have a reference to invalid memory, leading to a runtime error. Lifetime annotations ensure that we don’t violate the rules of borrowing and ownership in Rust, enabling us to write safe and correct code.

El artículo “Traducción automática neuronal mediante el aprendizaje conjunto de alineación y traducción ” propone un enfoque reciente para la traducción automática conocido como traducción automática neuronal (NMT). A diferencia de la traducción automática estadística tradicional, NMT busca construir una única red neuronal que pueda ser ajustada conjuntamente para maximizar el rendimiento de la traducción. Los modelos propuestos para NMT a menudo pertenecen a una familia de codificadores-decodificadores y constan de un codificador que codifica una oración fuente en un vector de longitud fija a partir del cual un decodificador genera una traducción. Los autores del artículo plantean que el uso de un vector de longitud fija es un cuello de botella para mejorar el rendimiento de esta arquitectura básica codificador-decodificador y proponen extenderla permitiendo que el modelo busque automáticamente (suave-) partes de una oración fuente que sean relevantes para predecir una palabra objetivo, sin tener que formar explícitamente estas partes como un segmento duro. Con este nuevo enfoque, se logra un rendimiento de traducción comparable al del sistema basado en frases del estado del arte existente en la tarea de traducción de inglés a francés. Lecciones aprendidas: La traducción automática neuronal es un enfoque reciente para la traducción automática que busca construir una única red neuronal que pueda ser ajustada conjuntamente para maximizar el rendimiento de la traducción. Los modelos propuestos para NMT a menudo pertenecen a una familia de codificadores-decodificadores y constan de un codificador que codifica una oración fuente en un vector de longitud fija a partir del cual un decodificador genera una traducción. El uso de un vector de longitud fija es un cuello de botella para mejorar el rendimiento de la arquitectura básica codificador-decodificador. Permitir que el modelo busque automáticamente partes relevantes de una oración fuente para predecir una palabra objetivo, sin tener que formar explícitamente estas partes como un segmento duro, puede mejorar significativamente el rendimiento de la traducción automática neuronal. Referencias: Bahdanau, D., Cho, K., & Bengio, Y. (2014). Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473.

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