Papers by Siamak Shakeri
End-to-End Synthetic Data Generation for Domain Adaptation of Question Answering Systems (2020.emnlp-main)
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Siamak Shakeri, Cicero Nogueira dos Santos, Henghui Zhu, Patrick Ng, Feng Nan, Zhiguo Wang, Ramesh Nallapati, Bing Xiang
| Challenge: | Existing approaches for synthetic QA data generation have limited or no success in improving the downstream Reading Comprehension task. |
| Approach: | They propose an end-to-end approach for synthetic QA data generation using a transformer-based encoder-decoder network that is trained end- to-end to generate both answers and questions. |
| Outcome: | The proposed model outperforms current state-of-the-art methods in the domain adaptation of QA models. |
Triggering Multi-Hop Reasoning for Question Answering in Language Models using Soft Prompts and Random Walks (2023.findings-acl)
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| Challenge: | Existing methods that decompose multi-hop questions into single hop sub-questions are difficult to implement. |
| Approach: | They propose to use random-walks to guide pre-trained language models to map multi-hop questions to random-walked paths that lead to the answer. |
| Outcome: | The proposed methods improve on two T5 LMs. |
ParsiNLU: A Suite of Language Understanding Challenges for Persian (2021.tacl-1)
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Daniel Khashabi, Arman Cohan, Siamak Shakeri, Pedram Hosseini, Pouya Pezeshkpour, Malihe Alikhani, Moin Aminnaseri, Marzieh Bitaab, Faeze Brahman, Sarik Ghazarian, Mozhdeh Gheini, Arman Kabiri, Rabeeh Karimi Mahabagdi, Omid Memarrast, Ahmadreza Mosallanezhad, Erfan Noury, Shahab Raji, Mohammad Sadegh Rasooli, Sepideh Sadeghi, Erfan Sadeqi Azer, Niloofar Safi Samghabadi, Mahsa Shafaei, Saber Sheybani, Ali Tazarv, Yadollah Yaghoobzadeh
| Challenge: | Despite progress in natural language understanding, most progress is concentrated on resource-rich languages like English . despite high-quality benchmarks, there are few available NLU datasets for Persian language . |
| Approach: | They propose a benchmark for Persian language that includes a range of language understanding tasks . they present their results on monolingual and multilingual pre-trained language models . |
| Outcome: | The proposed benchmarks compare human performance with monolingual and multilingual models on Persian language with high quality evaluation datasets. |
Transcending Scaling Laws with 0.1% Extra Compute (2023.emnlp-main)
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Yi Tay, Jason Wei, Hyung Chung, Vinh Tran, David So, Siamak Shakeri, Xavier Garcia, Steven Zheng, Jinfeng Rao, Aakanksha Chowdhery, Denny Zhou, Donald Metzler, Slav Petrov, Neil Houlsby, Quoc Le, Mostafa Dehghani
| Challenge: | Existing scaling of language models is expensive and requires significant computational costs. |
| Approach: | They propose a method that substantially improves existing language models and their scaling curves with a relatively tiny amount of extra compute. |
| Outcome: | The proposed method significantly improves existing language models and their scaling curves with a relatively tiny amount of extra compute. |
Knowledge Graph Based Synthetic Corpus Generation for Knowledge-Enhanced Language Model Pre-training (2021.naacl-main)
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| Challenge: | Existing work on data-to-text generation focused on domain-specific benchmark datasets. |
| Approach: | They use a KG-Wikipedia text aligned corpus to verbalize the entire English Wikidata KG . they show that this approach can be used to integrate structured KGs and natural language corpora . |
| Outcome: | The proposed method improves on open domain QA and the LAMA knowledge probe. |