Papers by Natasa Milic-Frayling
Contextual Knowledge Learning for Dialogue Generation (2023.acl-long)
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| Challenge: | Incorporating conversational context and knowledge into dialogue generation models has been essential for improving the quality of the generated responses. |
| Approach: | They propose a method to incorporate conversational context and knowledge into dialogue generation models . they use Latent Vectors to capture the relationship between context and knowing . |
| Outcome: | The proposed approach improves performance with two standard datasets and human evaluations. |
LAraBench: Benchmarking Arabic AI with Large Language Models (2024.eacl-long)
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Ahmed Abdelali, Hamdy Mubarak, Shammur Chowdhury, Maram Hasanain, Basel Mousi, Sabri Boughorbel, Samir Abdaljalil, Yassine El Kheir, Daniel Izham, Fahim Dalvi, Majd Hawasly, Nizi Nazar, Youssef Elshahawy, Ahmed Ali, Nadir Durrani, Natasa Milic-Frayling, Firoj Alam
| Challenge: | Recent advances in Large Language Models (LLMs) have significantly influenced the landscape of language and speech research. |
| Approach: | They used GPT-3.5-turbo, GPT-4, BLOOMZ, Jais-13b-chat, Whisper, and USM to tackle 33 distinct tasks across 61 datasets. |
| Outcome: | The proposed model outperforms SOTA models in zero-shot learning, with a few exceptions. |
Approximation of Response Knowledge Retrieval in Knowledge-grounded Dialogue Generation (2020.findings-emnlp)
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| Challenge: | Recent studies have focused on improving dialogue generation models that include knowledge related to the posts. |
| Approach: | They propose to use a novel method to generate responses from posts and related knowledge by injecting knowledge into dialogue generation models. |
| Outcome: | The proposed method outperforms baseline models in terms of knowledge relevance and quality. |
Knowledge-Grounded Dialogue Generation with Term-level De-noising (2021.findings-acl)
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| Challenge: | Existing methods for dialogue generation use terms to describe a post, such as 'question', 'utterance','source' and 'query', but this approach introduces noise and diminishes the effectiveness of the generative models. |
| Approach: | They propose a Knowledge Term Weighting Model that incorporates term-level de-noising of the selected knowledge into the model. |
| Outcome: | The proposed model achieves statistically significant improvements over methods without term weighting on two publicly available datasets Wizard of Wikipedia and Holl-E. |