Customer-obsessed science
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July 9, 202610 min readA new Rust proxy called Turnstile sits between the model backend and the agent harness to capture information lost in mere text transcripts.
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Featured news
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KDD 2023 Workshop on Machine Learning in Finance (MLF)2023Subledgers maintain detailed information about specific accounts or transactions in order to substantiate the general ledger. Subledgers provide a granular level of detail for financial reporting and analysis, which is especially essential for accounts receivables and payables. The size of subledgers can vary greatly depending on the complexity and volume of transactions and their size can also increase
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KDD 2023 Workshop on Robust NLP for Finance (RobustFin)2023In large corporations, millions of cash transactions are booked via cash management software (CMS) per month. Most CMS systems adopt a key-word (search string) based matching logic for booking, which checks if the cash transaction description contains a specific search string and books the transaction to an appropriate general ledger account (GL-account) according to a booking rule. However, due to the
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Interspeech 20232023In this work, we introduce a diffusion-based text-to-speech (TTS) system for accent modelling. TTS systems have become a natural part of our surroundings. Nevertheless, because of the complexity of accent modelling, recent state-of-the-art solutions mainly focus on the most common variants of each language. In this work, we propose to address this issue with a newly proposed diffusion generative model (
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ACL Findings 20232023Multilingual information retrieval (IR) is challenging since annotated training data is costly to obtain in many languages. We present an effective method to train multilingual IR systems when only English IR training data and some parallel corpora between English and other languages are available. We leverage parallel and non-parallel corpora to improve the pretrained multilingual language models’ cross-lingual
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IEEE ICIP 20232023Learning product similarity using distance metric learning from real world catalog needs to take care of large number of product categories and noisy labels. On one hand, large number of product categories makes online hard mining (OHM) less effective as hard triplets become sparse and thus difficult to find. On the other hand, the validity of the hard-triplets themselves is less certain in the case of
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