2024 Text matching with richer alignment features

Text matching with richer alignment features

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Web28 Oct 2024 · Simple and Effective Text Matching with Richer Alignment Features. Conference Paper. Jan 2024; Runqi Yang; Jianhai Zhang; Xing Gao; Haiqing Chen; View. Web1 Aug 2024 · We explore what is sufficient to build a fast and well-performed text matching model and propose to keep three key features available for inter-sequence alignment: original point-wise features, previous aligned features, and contextual features while simplifying all the remaining components.

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Top PDF Simple and Effective Text Matching with Richer Alignment Features

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Matching Text with Deep Mutual Information Estimation DeepAI

WebUS20240060787A1 US17/915,011 US202417915011A US2024060787A1 US 20240060787 A1 US20240060787 A1 US 20240060787A1 US 202417915011 A US202417915011 A US 202417915011A US 2024060787 A Web17 May 2024 · Text matching is an important task in natural language processing (NLP) and is widely used in information retrieval, QA systems, chatbots, machine translation, recommendation systems, machine reading and so on. Unlike textual similarity, text matching is more concerned with exploring the logical relationship between texts. change hibernate time hard rock cafe ft lauderdale concerts

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Text matching with richer alignment features

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Web14 Jul 2024 · It simplifies many slow components which are previously considered as core building blocks in text matching, while keeping three key features directly available for inter-sequence alignment: original point-wise features, previous aligned features, and … WebA pytorch implementation of the ACL2024 paper "Simple and Effective Text Matching with Richer Alignment Features". Support. Quality. Security. License. Reuse. Support. Quality. …

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Web11 Oct 2024 · In a text matching task, a model takes two text sequences as input and predicts their relationship. This method aims to explore what is sufficient for strong … WebMore effective models can be built if inter- sequence matching is allowed to be performed more than once. CSRAN (Tay et al., 2024a) per- forms multi-level attention refinement with dense connections among multiple levels. DRCN (Kim et al., 2024) stacks encoding and alignment lay- ers. It concatenates all previously aligned results and has to use an …

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