Papers by Afshin Rahimi
Fairness-aware Class Imbalanced Learning (2021.emnlp-main)
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| Challenge: | Existing studies on class imbalance and mitigating bias have focused on the latter . a skewed class distribution hurts the performance of deep learning models, and is often referred to as "stereotyping" |
| Approach: | They propose to extend a margin-loss based approach to enforce fairness by using tweet sentiment and occupation classification to mitigate class imbalance and demographic bias. |
| Outcome: | The proposed methods help mitigate class imbalance and demographic biases through controlled experiments. |
Semi-supervised User Geolocation via Graph Convolutional Networks (P18-1)
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| Challenge: | Social media user geolocation is vital to many applications such as event detection. |
| Approach: | They propose a multiview geolocation model that uses both text and network context. |
| Outcome: | The proposed model outperforms baseline models and the state-of-the-art models under minimal supervision. |
Massively Multilingual Transfer for NER (P19-1)
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| Challenge: | Existing approaches for cross-lingual transfer use a single source language, but there are exceptions. |
| Approach: | They propose two techniques for modulating the transfer, suitable for zero-shot or few-shot learning, respectively. |
| Outcome: | The proposed methods are much more effective than baseline models and rival oracle selection of the single best individual model. |
WikiUMLS: Aligning UMLS to Wikipedia via Cross-lingual Neural Ranking (2020.coling-main)
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| Challenge: | Using a neural reranking model, we can match a UMLS concept with a Wikipedia page, enabling manual alignment with minimal effort. |
| Approach: | They propose a cross-lingual neural reranking model to match a UMLS concept with a Wikipedia page, which achieves a recall@1of 72%, a substantial improvement of 20% over word- and char-level BM25. |
| Outcome: | The proposed model achieves recall@1of 72%, 20% better than word- and char-level BM25, and will facilitate easier access to Wikipedia for health professionals, patients, and NLP systems, including in multilingual settings. |
IndoLEM and IndoBERT: A Benchmark Dataset and Pre-trained Language Model for Indonesian NLP (2020.coling-main)
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| Challenge: | despite being spoken by 200 million people, the Indonesian language is underrepresented in NLP research. |
| Approach: | They propose a dataset for Indonesian that includes seven NLP tasks . they also propose 'indonesian language evaluation Montage' tasks that are based on previous work . |
| Outcome: | The proposed dataset shows that IndoBERT outperforms IndoLEM over most of the tasks. |