Improving Sentence-final Verb Prediction in Japanese using Recurrent Neural Networks and Sentence Shuffling
Haverford College. Department of Computer Science
Place of Publication
Table of Contents
Dark Archive until 2026-01-01, afterwards Open Access.
This thesis is about analyzing the different approaches to verb prediction in machine translation, mainly between languages with different grammar structures. We will introduce the importance of verb prediction in translation between Subject-Object- Verb languages and Subject-Verb-Object languages. There will be summaries of recent works on using different models like Reinforcement Learning and Regression in classifying the verbs, and strategies like sentence rewriting to achieve better performance insimultaneous translation. Then we use different models with different approaches to do similar tasks in previous papers. We first do a near replication of model in Grissom II et al. 2016. Besides that, in order to see the importance of the final case marker in Japanesefinal verb prediction, we train the model to do the classification without the final case marker. We also use an LSTM model and a BiGRU model to do the same task in the aforementioned paper and analyze the different results from the previous model. Since sentence structure is relatively free in Japanese, we shuffle the POS tokens and KNP bunsetsu to see if adding shuffled sentences in the dataset can increase the accuracy. Besides doing a 4 choice multiple choice task as Grissom II et al. 2016, we also conduct experiments on the LSTM and BiGRU models to do a multiple choice task with 50 and 100 verbs to see their general prediction among more verbs.
Amberley (Zhan) Su was a Bryn Mawr College student.