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Titlebook: Masters Theses in the Pure and Applied Sciences; Accepted by Colleges Wade H. Shafer Book 1986 Plenum Press, New York 1986 astronomy.calcul

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Wade H. Shaferng MWPs with two types of solvers: tree-based solver and large language model (LLM) solver. However, these approaches always solve MWPs by a single solver, which will bring the following problems: (1) Single type of solver is hard to solve all types of MWPs well. (2) A single solver will result in p
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Wade H. Shafers of TCM relies heavily on doctors’ experience, which can affect the accuracy of diagnosis in practice. With the development of natural language processing technology, its mechanism can learn from a large amount of unstructured text to obtain a comprehensive and unified classification model. In this
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Wade H. Shaferiety of reasoning tasks. Still, due to the demand for low costs, research on the upper bound of small language models in reasoning tasks and the limitation of the knowledge they can accommodate has drawn attention. In line with previous work on math word problems, we discover that models that only l
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Wade H. Shafer (D-CPT) structure is proposed to better represent the dependency relations in a CPT-style structure, which employs dependency relation types instead of phrase labels in CPT. In this way, D-CPT not only keeps the dependency relationship information in the dependency parse tree (DPT) structure but al
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Wade H. Shaferords and thus lead to the loss of some important semantics. In this paper, we propose a new method to exploit word structure and integrate lexical semantics into character representations of pre-trained models. Specifically, we project a word’s embedding into its internal characters’ embeddings acco
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Wade H. Shaferso very brittle and easily falter when fed with noisy sentences, i.e., from automatic speech recognition (ASR) output. Due to the lack of Chinese-to-English translation test set with natural Chinese-side ASR output, related studies artificially add noise into Chinese sentences to evaluation translat
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