Data Augmentation with Seq2Seq Models
| dc.contributor.author | Granstedt, Jason Louis | en |
| dc.contributor.committeechair | Batra, Dhruv | en |
| dc.contributor.committeemember | Baumann, William T. | en |
| dc.contributor.committeemember | Huang, Bert | en |
| dc.contributor.department | Electrical and Computer Engineering | en |
| dc.date.accessioned | 2017-07-07T08:00:35Z | en |
| dc.date.available | 2017-07-07T08:00:35Z | en |
| dc.date.issued | 2017-07-06 | en |
| dc.description.abstract | Paraphrase sparsity is an issue that complicates the training process of question answering systems: syntactically diverse but semantically equivalent sentences can have significant disparities in predicted output probabilities. We propose a method for generating an augmented paraphrase corpus for the visual question answering system to make it more robust to paraphrases. This corpus is generated by concatenating two sequence to sequence models. In order to generate diverse paraphrases, we sample the neural network using diverse beam search. We evaluate the results on the standard VQA validation set. Our approach results in a significantly expanded training dataset and vocabulary size, but has slightly worse performance when tested on the validation split. Although not as fruitful as we had hoped, our work highlights additional avenues for investigation into selecting more optimal model parameters and the development of a more sophisticated paraphrase filtering algorithm. The primary contribution of this work is the demonstration that decent paraphrases can be generated from sequence to sequence models and the development of a pipeline for developing an augmented dataset. | en |
| dc.description.abstractgeneral | For a machine, processing language is hard. All possible combinations of words in a language far exceed a computer’s ability to directly memorize them. Thus, generalizing language into a form that a computer can reason with is necessary for a machine to understand raw human input. Various advancements in machine learning have been particularly impressive in this regard. However, they require a corpus, or a body of information, in order to learn. Collecting this corpus is typically expensive and time consuming, and does not necessarily contain all of the information that a system would need to know - the machine would not know how to handle a word that it has never seen before, for example. This thesis examines the possibility of using a large, general corpus to expand the vocabulary size of a specialized corpus in order to improve performance on a specific task. We use Seq2Seq models, a recent development in neural networks that has seen great success in translation tasks to do so. The Seq2Seq model is trained on the general corpus to learn the language and then applied to the specialized corpus to generate paraphrases similar to the format in the specialized corpus. We were able to significantly expand the volume and vocabulary size of the specialized corpus via this approach, we have demonstrated that decent paraphrases can be generated from Seq2Seq models, and we developed a pipeline for augmenting other specialized datasets. | en |
| dc.description.degree | Master of Science | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:10139 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/78315 | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | Data Augmentation | en |
| dc.subject | Seq2Seq | en |
| dc.subject | Diverse Beam Search | en |
| dc.subject | VQA | en |
| dc.title | Data Augmentation with Seq2Seq Models | en |
| dc.type | Thesis | en |
| thesis.degree.discipline | Electrical Engineering | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | masters | en |
| thesis.degree.name | Master of Science | en |
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