Hassan Sajjad
Biographic Data
| ID | 6536967 |
|---|---|
| NAME | Hassan Sajjad |
| GIVEN NAMES | Hassan |
| FAMILY NAME | Sajjad |
| SIGNATURE | SAJJAD H |
| AFFILIATIONS | Hamad bin Khalifa University |
| ORCID | 0000-0002-8584-6595 |
| VERIFIED | Yes |
| TOTAL WORKS | 4 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 4 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2015 |
| LATEST PUBLICATION YEAR | 2022 |
| H-INDEX | 0 |
When a disaster happens, we are ready
On the Linguistic Representational Power of Neural Machine Translation Models
Despite the recent success of deep neural networks in natural language processing and other spheres of artificial intelligence, their interpretability remains a challenge. We analyze the representations learned by neural machine translation (NMT) models at various levels of granularity and evaluate their quality through relevant extrinsic properties. In particular, we seek answers to the following questions: (i) How accurately is word structure c…
Statistical Models for Unsupervised, Semi-Supervised, and Supervised Transliteration Mining
We present a generative model that efficiently mines transliteration pairs in a consistent fashion in three different settings: unsupervised, semi-supervised, and supervised transliteration mining. The model interpolates two sub-models, one for the generation of transliteration pairs and one for the generation of non-transliteration pairs (i.e., noise). The model is trained on noisy unlabeled data using the EM algorithm. During training the trans…
Bridging social media via distant supervision
No prominent works on this page.
Bridging social media via distant supervision
Statistical Models for Unsupervised, Semi-Supervised, and Supervised Transliteration Mining
We present a generative model that efficiently mines transliteration pairs in a consistent fashion in three different settings: unsupervised, semi-supervised, and supervised transliteration mining. The model interpolates two sub-models, one for the generation of transliteration pairs and one for the generation of non-transliteration pairs (i.e., noise). The model is trained on noisy unlabeled data using the EM algorithm. During training the trans…
On the Linguistic Representational Power of Neural Machine Translation Models
Despite the recent success of deep neural networks in natural language processing and other spheres of artificial intelligence, their interpretability remains a challenge. We analyze the representations learned by neural machine translation (NMT) models at various levels of granularity and evaluate their quality through relevant extrinsic properties. In particular, we seek answers to the following questions: (i) How accurately is word structure c…
When a disaster happens, we are ready
Computer Science (3 works) · Artificial Intelligence (2 works) · Natural language processing (2 works) · Natural Language Processing Techniques (2 works) · Social media (2 works) · Topic Modeling (2 works) · Word (group theory (2 works) · Artificial neural network (1 work) · Computer security (1 work) · Contrast (vision (1 work)