| Budget Amount *help |
¥17,160,000 (Direct Cost: ¥13,200,000、Indirect Cost: ¥3,960,000)
Fiscal Year 2021: ¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000)
Fiscal Year 2020: ¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000)
Fiscal Year 2019: ¥3,900,000 (Direct Cost: ¥3,000,000、Indirect Cost: ¥900,000)
Fiscal Year 2018: ¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
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| Outline of Final Research Achievements |
The aim of this research was to develop a CCG syntactic parser based on a hybrid method of theoretical linguistics and deep neural networks. We successfully formulated and implemented RNN-CCG, a system that converts the grammatical theory of RNNG from CFG to CCG. We also studied automatic theorem prover with Dependent-Type Semantics (DTS) and successfully implemented a proof search algorithm by Haskell. In parallel, we also conducted research to evaluate the performance of natural language inference tasks with large-scale language models and showed their limitations, and empirical research in theoretical linguistics using dependent tye semantics, such as comparative constructions, Weak Crossover and the Proviso problem.
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