Papers by Fahimeh Saleh
Multilingual Neural Machine Translation: Can Linguistic Hierarchies Help? (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Multilingual Neural Machine Translation (MNMT) trains a single model that supports translation between multiple languages . transferring knowledge from a diverse set of languages degrades the translation performance due to negative transfer. |
| Approach: | They propose a hierarchical knowledge distillation approach to train multilingual models . they use typological features and phylogeny to overcome negative transfer issue . |
| Outcome: | The proposed approach avoids negative transfer effect by capitalising on language groups generated according to typological features and phylogeny of languages. |
Naver Labs Europe’s Systems for the Document-Level Generation and Translation Task at WNGT 2019 (D19-56)
Copied to clipboard
| Challenge: | Recent advances in machine translation and natural language generation have created many challenges in this field especially when context is considered. |
| Approach: | They propose to leverage data from machine translation and natural language generation tasks to do transfer learning between MT, NLG and MT with source-side metadata. |
| Outcome: | The proposed approach outperforms the previous state-of-the-art on the Rotowire NLG task. |
Collective Wisdom: Improving Low-resource Neural Machine Translation using Adaptive Knowledge Distillation (2020.coling-main)
Copied to clipboard
| Challenge: | Existing approaches to train high-quality NMT models in bilingually low-resource scenarios are limited by the scarcity of parallel sentence-pairs. |
| Approach: | They propose to distill the knowledge of teacher models to a single student model by using knowledge distillation. |
| Outcome: | The proposed approach achieves up to +0.9 BLEU score improvements compared to strong baselines. |
CLARITY: A Framework and Benchmark for Conversational Language Ambiguity and Unanswerability in Interactive NL2SQL Systems (2026.acl-industry)
Copied to clipboard
Tabinda Sarwar, Farhad Moghimifar, Cong Duy Vu Hoang, Xiaoxiao Ma, Shawn Chang Xu, Fahimeh Saleh, Poorya Zaremoodi, Avirup Sil, Katrin Kirchhoff
| Challenge: | Existing benchmarks assume a single source of ambiguity and rely on user interaction for resolution, overlooking realistic failure modes. |
| Approach: | They propose a framework for automatically generating an NL2SQL benchmark with multi-faceted ambiguities and diverse user behaviors. |
| Outcome: | The proposed framework transforms executable SQL into ambiguous queries with a conversational continuation and schema-level metadata. |