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Victor Carbune

Victor Carbune

Joined as a SWE in Google Research Europe in 2014. Master's degree from ETH Zürich, Learning and Adaptive Systems Group and Bachelor's degree from Politehnica University of Bucharest, Romania. Research interests: Sequence Modeling, Deep Learning and Reinforcement Learning.
Authored Publications
Google Publications
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    Towards Better Evaluation of Instruction-Following: A Case-Study in Summarization
    Rahul Aralikatte
    Sian Gooding
    Proceedings of the 27th Conference on Computational Natural Language Learning (CoNLL), Association for Computational Linguistics (2023)
    Preview abstract Despite recent advances, evaluating how well large language models (LLMs) follow user instructions remains an open problem. While evaluation methods of language models have seen a rise in prompt-based approaches, limited work on the correctness of these methods has been conducted. In this work, we perform a meta-evaluation of a variety of metrics to quantify how accurately they measure the instruction-following abilities of LLMs. Our investigation is performed on grounded query-based summarization by collecting a new short-form, real-world dataset riSum, containing 300 document-instruction pairs with 3 answers each. All 900 answers are rated by 3 human annotators. Using riSum, we analyze the agreement between evaluation methods and human judgment. Finally, we propose new LLM-based reference-free evaluation methods that improve upon established baselines and perform on par with costly reference-based metrics that require high-quality summaries. View details
    Bogdan Prisacari
    Daria Soboleva
    Felix Weissenberger
    Justin Lu
    Márius Šajgalík
    ICASSP 2021: International Conference on Acoustics, Speech and Signal Processing (2021) (to appear)
    Preview abstract We present a novel multi-modal unspoken punctuation prediction system for the English language, which relies on Quasi-Recurrent Neural Networks (QRNNs) applied jointly on the text output from automatic speech recognition and acoustic features. % We show significant improvements from adding acoustic features compared to the text-only baseline. Because annotated acoustic data is hard to obtain, we demonstrate that relying on only 20% of human-annotated audio and replacing the rest with synthetic text-to-speech (TTS) predictions, does not suffer from quality loss on LibriTTS corpus. % Furthermore, we demonstrate that through data augmentation using TTS models, we can remove human-recorded audio completely and outperform models trained on it. View details
    Fast Multi-language LSTM-based Online Handwriting Recognition
    Thomas Deselaers
    Alexander Daryin
    Marcos Calvo
    Sandro Feuz
    Philippe Gervais
    International Journal on Document Analysis and Recognition (IJDAR) (2020)
    Preview abstract Handwriting is a natural input method for many people and we continuously invest in improving the recognition quality. Here we describe and motivate the modelling and design choices that lead to a significant improvement across the 100 supported languages, based on recurrent neural networks and a variety of language models. % This new architecture has completely replaced our previous segment-and-decode system~\cite{Google:HWRPAMI} and reduced the error rate by 30\%-40\% relative for most languages. Further, we report new state-of-the-art results on \iamondb for both the open and closed dataset setting. % By using B\'ezier curves for shortening the input length of our sequences we obtain up to 10x faster recognition times. Through a series of experiments we determine what layers are needed and how wide and deep they should be. % We evaluate the setup on a number of additional public datasets. % View details
    SmartChoices: Hybridizing Programming and Machine Learning
    Alexander Daryin
    Thomas Deselaers
    Nikhil Sarda
    Reinforcement Learning for Real Life (RL4RealLife) Workshop in the 36th International Conference on Machine Learning (ICML), (2019)
    Preview abstract We present SmartChoices, an approach to making machine learning (ML) a first class citizen in programming languages which we see as one way to lower the entrance cost to applying ML to problems in new domains. There is a growing divide in approaches to building systems: on the one hand, programming leverages human experts to define a system while on the other hand behavior is learned from data in machine learning. We propose to hybridize these two by providing a 3-call API which we expose through an object called SmartChoice. We describe the SmartChoices-interface, how it can be used in programming with minimal code changes, and demonstrate that it is an easy to use but still powerful tool by demonstrating improvements over not using ML at all on three algorithmic problems: binary search, QuickSort, and caches. In these three examples, we replace the commonly used heuristics with an ML model entirely encapsulated within a SmartChoice and thus requiring minimal code changes. As opposed to previous work applying ML to algorithmic problems, our proposed approach does not require to drop existing implementations but seamlessly integrates into the standard software development workflow and gives full control to the software developer over how ML methods are applied. Our implementation relies on standard Reinforcement Learning (RL) methods. To learn faster, we use the heuristic function, which they are replacing, as an initial function. We show how this initial function can be used to speed up and stabilize learning while providing a safety net that prevents performance to become substantially worse -- allowing for a safe deployment in critical applications in real life. View details
    Multi-Language Online Handwriting Recognition
    Thomas Deselaers
    IEEE Transactions on Pattern Analysis and Machine Intelligence (2016)
    Preview abstract We describe Google's online handwriting recognition system that currently supports 22 scripts and 97 languages. The system's focus is on fast, high-accuracy text entry for mobile, touch-enabled devices. We use a combination of state-of-the-art components and combine them with novel additions in a flexible framework. This architecture allows us to easily transfer improvements between languages and scripts. This made it possible to build recognizers for languages that, to the best of our knowledge, are not handled by any other online handwriting recognition system. The approach also enabled us to use the same architecture both on very powerful machines for recognition in the cloud as well as on mobile devices with more limited computational power by changing some of the settings of the system. In this paper we give a general overview of the system architecture and the novel components, such as unified time- and position-based input interpretation, trainable segmentation, minimum-error rate training for feature combination, and a cascade of pruning strategies. We present experimental results for different setups. The system is currently publicly available in several Google products, for example in Google Translate and as an input method for Android devices. View details
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