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Register a new userModeling Human Sentence Processing with Left-Corner Recurrent Neural Network Grammars
نمذجة تجهيز الجملة المعالجة مع قواعد الركن الناجم عن الشبكة العصبية المتكررة
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في اللغويات الحسابية، فقد تبين أن الهياكل الهرمية تجعل نماذج اللغة (LMS) أكثر تشبه الإنسان. ومع ذلك، فإن الأدب السابق كان غير ملائم حول استراتيجية تحليل النماذج الهرمية. في هذه الورقة، قامنا بالتحقيق في ما إذا كانت الهياكل الهرمية تجعل LMS أكثر تشبه الإنسان، وإذا كان الأمر كذلك، ما هي استراتيجية التحليل أكثر منطقية. من أجل معالجة هذا السؤال، قمنا بتقييم ثلاثة LMS ضد أوقات القراءة البشرية باللغة اليابانية مع هياكل المتفرعة اليسرى في الرأس: ذاكرة طويلة الأجل الطويلة (LSTM) كطراز متتابع ونواسيب الشبكة العصبية المتكررة (RNNGS) مع أعلى إلى أسفل واستراتيجيات تحليل الركن الأيسر كنماذج هرمية. أظهرت النمذجة الحاسوبية لدينا أن RNNGS الركن الأيسر تفوقت على RNNGS و LSTM من أعلى إلى أسفل، مما يشير إلى أن هياكل التسلسل الهرمي واليسرى من المعقول أكثر منطقية أكثر من الأعلى إلى أسفل أو هندسة متسلسلة. بالإضافة إلى ذلك، سيتم مناقشة العلاقات بين المعقول المعرفي و (1) حيرة، (2) تحليل، و (III) بحجم شعاع.
In computational linguistics, it has been shown that hierarchical structures make language models (LMs) more human-like. However, the previous literature has been agnostic about a parsing strategy of the hierarchical models. In this paper, we investigated whether hierarchical structures make LMs more human-like, and if so, which parsing strategy is most cognitively plausible. In order to address this question, we evaluated three LMs against human reading times in Japanese with head-final left-branching structures: Long Short-Term Memory (LSTM) as a sequential model and Recurrent Neural Network Grammars (RNNGs) with top-down and left-corner parsing strategies as hierarchical models. Our computational modeling demonstrated that left-corner RNNGs outperformed top-down RNNGs and LSTM, suggesting that hierarchical and left-corner architectures are more cognitively plausible than top-down or sequential architectures. In addition, the relationships between the cognitive plausibility and (i) perplexity, (ii) parsing, and (iii) beam size will also be discussed.
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