Papers by Md. Akmal Haidar
Improving Word Embedding Factorization for Compression Using Distilled Nonlinear Neural Decomposition (2020.findings-emnlp)
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| Challenge: | Word-embeddings are vital components of natural language processing (NLP) but they consume a lot of memory which poses a challenge for edge deployment. |
| Approach: | They propose an embedding compression method based on matrix decomposition and knowledge distillation that initializes weights of pre-trained word-embeddings and fine-tunes end-to-end. |
| Outcome: | The proposed method has higher BLEU score on translation and lower perplexity on language modeling compared to complex, difficult to tune methods. |
Latent Code and Text-based Generative Adversarial Networks for Soft-text Generation (N19-1)
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| Challenge: | Text generation with generative adversarial networks (GANs) can be divided into text-based and code-based categories depending on the type of signals used for discrimination. |
| Approach: | They propose a text-based approach to exploit generative adversarial networks (GANs) by using autoencoders to provide a continuous representation of sentences, which they will refer to as soft-text, and hybrid latent code and text-oriented approaches with one or more discriminators. |
| Outcome: | The proposed approach outperforms the traditional GAN-based methods on two well-known datasets. |