Papers by Abdul Rafae Khan
Measuring Robustness for NLP (2022.coling-1)
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| Challenge: | Existing methods to evaluate NLP models are limited to news domains and cannot be generalized to other domains. |
| Approach: | They propose a measure of NLP quality based on robustness . they measure consistency of cross-domain accuracy and introduce coefficient of variation and gamma-Robustness based upon human evaluation . |
| Outcome: | The proposed approach shows higher agreement with human evaluation than accuracy scores on ranking machine translation systems. |
Probabilistic Robustness for Data Filtering (2023.eacl-main)
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| Challenge: | Modern machine learning works with massive amounts of data on a range of tasks like language modeling, object detection, and data mining. |
| Approach: | They propose a probabilistic robustness rewarded data optimization approach to enhance the model's generalization power by selecting training data that optimizes probabilistic metrics. |
| Outcome: | The proposed approach achieves +17.2% increase of accuracy and -28.05 decrease of perplexity on unknown-domain test sets. |
Coding Textual Inputs Boosts the Accuracy of Neural Networks (2020.emnlp-main)
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| Challenge: | a new approach to natural language processing uses arbitrary symbols to represent meaning . Soundex, MetaPhone, NYSIIS, logogram are used as inputs for NLP . |
| Approach: | They propose to use arbitrary symbols to represent linguistic meaning of a word . they propose to integrate codewords with text to provide more reliable inputs . |
| Outcome: | The proposed approach outperforms state-of-the-art models on machine translation, language modeling, and part-of speech tagging. |
Grouping Words with Semantic Diversity (2021.naacl-main)
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| Challenge: | a new approach to grouping input words based on their semantic diversity is proposed . high-dimensional inputs and learning complexity hinders deep learning generalization, authors say . |
| Approach: | They propose a way to group input words based on their semantic diversity to simplify input language representation with low ambiguity. |
| Outcome: | The proposed methods generalize NLP models and demonstrate improvements on medium-scale machine translation tasks. |