Papers by Masud Moshtaghi
Supervised and Nonlinear Alignment of Two Embedding Spaces for Dictionary Induction in Low Resourced Languages (D19-1)
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| Challenge: | Existing methods for mapping monolingual word embeddings into another are based on anchor points and unsupervised methods are more adversarial. |
| Approach: | They propose a noise-tolerant piecewise linear technique to learn a non-linear mapping between two monolingual word embedding vector spaces. |
| Outcome: | The proposed method outperforms the state-of-the-art in lower resourced settings with an average of 3.7% improvement of precision @10 across 14 mostly low resourced languages. |
Controlled Text Generation with Hidden Representation Transformations (2023.findings-acl)
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| Challenge: | Using a con-trolled language model, we gain attribute control by modifying the hidden representation of thebase model through learning transformations. |
| Approach: | They propose a con-trolled language generation framework that gains attribute control bymodifying the hidden representation of thebase model through learned transformations. |
| Outcome: | The proposed framework outperforms all thebaselines in detoxification, positivesentiment steering, and text simplification while minimizing the loss in linguistic qualities. |
APEX-MEM: Agentic Semi-Structured Memory with Temporal Reasoning for Long-Term Conversational AI (2026.acl-long)
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| Challenge: | Large language models struggle with reliable long-term conversational memory . enlarging context windows or applying nave retrieval often introduces noise . |
| Approach: | They propose a conversational memory system that uses domain-agnostic ontology to structure conversations as temporally grounded events in an entity-centric framework. |
| Outcome: | APEX-MEM outperforms state-of-the-art retrieval methods in accuracy and time resolution. |