Papers by Mohammad Amin
BanLemma: A Word Formation Dependent Rule and Dictionary Based Bangla Lemmatizer (2023.findings-emnlp)
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Sadia Afrin, Md. Shahad Mahmud Chowdhury, Md. Islam, Faisal Khan, Labib Chowdhury, Md. Mahtab, Nazifa Chowdhury, Massud Forkan, Neelima Kundu, Hakim Arif, Mohammad Mamun Or Rashid, Mohammad Amin, Nabeel Mohammed
| Challenge: | Lemmatization holds significance in both natural language processing (NLP) and linguistics due to the highly inflected nature and morphological richness of Bangla text. |
| Approach: | They propose linguistic rules for lemmatization and utilize a dictionary along with the rules to design a lemma specifically for Bangla. |
| Outcome: | The proposed system achieves 96.36% accuracy when tested against a manually annotated test dataset. |
CAUSE: Counterfactual Assessment of User Satisfaction Estimation in Task-Oriented Dialogue Systems (2024.findings-acl)
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| Challenge: | a number of studies have evaluated user satisfaction estimation in TOD systems . current benchmarks for user satisfaction estimates are highly skewed towards dialogues for which the user is satisfied. |
| Approach: | They leverage large language models to generate satisfaction-aware counterfactual dialogues to augment original dialogues of a test collection. |
| Outcome: | The proposed models show higher robustness to increase in dissatisfaction labels than fine-tuned models. |
E-LANG: Energy-Based Joint Inferencing of Super and Swift Language Models (2022.acl-long)
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| Challenge: | E-LANG is an efficient method for building large and highly capable language models . existing methods are only applicable to encoder-only backbones and classification tasks . |
| Approach: | They propose an efficient dynamic inference approach which distributes inference between large accurate Super-models and light-weight Swift models. |
| Outcome: | The proposed method outperforms existing methods on GLUE, SuperGLUE and WMT with 3.3X computation speed and 2.9X computation cost. |
SOLID: Self-seeding and Multi-intent Self-instructing LLMs for Generating Intent-aware Information-Seeking Dialogs (2025.findings-naacl)
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Arian Askari, Roxana Petcu, Chuan Meng, Mohammad Aliannejadi, Amin Abolghasemi, Evangelos Kanoulas, Suzan Verberne
| Challenge: | Existing methods for intent prediction rely on human feedback and are tailored to structured intents. |
| Approach: | They propose a method that generates dialogs turn-by-turn using self-seeding and multi-intent self-instructing strategies. |
| Outcome: | The proposed methods generate dialogs turn-by-turn using self-seeding and multi-intent self-instructing strategies. |