Papers by Pranaydeep Singh
Lemmatisation of Medieval Greek: Against the Limits of Transformer’s Capabilities? (2024.lrec-main)
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| Challenge: | Existing lemmatisation algorithms display an accuracy drop of around 30pp when tested on unedited, Byzantine Greek epigrams. |
| Approach: | They propose to use transformer-based embeddings and a dictionary look-up to lemmatise unedited, Byzantine Greek epigrams. |
| Outcome: | The proposed method outperforms existing methods and provides detailed error analysis revealing why unedited, Byzantine Greek is so challenging for lemmatisation. |
When the Student Becomes the Master: Learning Better and Smaller Monolingual Models from mBERT (2022.coling-1)
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| Challenge: | Using a jointly trained model for 102 languages, monolingual models outperform the original model. |
| Approach: | They propose to distill monolingual models from a jointly trained model for 102 languages using a text corpus of 160 GB. |
| Outcome: | The proposed model outperforms the original model for 6 languages with varying amounts of resources and language families. |
Misery Loves Complexity: Exploring Linguistic Complexity in the Context of Emotion Detection (2023.findings-emnlp)
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| Challenge: | a negative emotion is a cognitive bias that affects how we express thoughts and opinions online . a recent study shows that negative words generate more engagement and clicks than positive ones . |
| Approach: | They propose to use readability and linguistic complexity metrics to better understand emotions . they propose to fine-tune three state-of-the-art transformers to detect emotions based on a dataset . |
| Outcome: | The proposed model fails to predict emotions on complex texts, the authors show . they also show that more advanced models fail to predict complex texts . |
EnerGIZAr: Leveraging GIZA++ for Effective Tokenizer Initialization (2025.findings-acl)
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| Challenge: | Continual pre-training has long been considered the default strategy for adapting models to non-English languages, but struggles with initializing new embeddings, especially for non-Latin scripts. |
| Approach: | They propose a method that leverages statistical word alignment techniques to improve continual pre-training by leveraging word alignment matrix between source and target tokens. |
| Outcome: | The proposed method outperforms existing methods on key NLP tasks including POS tagging, Sentiment Analysis, NLI, and NER in Hindi, Basque, Arabic and Korean. |
Lost in Activations: A Neuron-level Analysis of Encoders for Cross-Lingual Emotion Detection (2026.eacl-short)
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| Challenge: | XLM-R models for multilingual emotion classification are still lacking in understanding of their internal decision-making mechanisms. |
| Approach: | They propose to use neuron-level activation analysis to study the inter-language differences between neurons. |
| Outcome: | The proposed model consistently encodes emotion-related concepts across languages, but others show strong monolingual specialization. |
Identifying Cognates in English-Dutch and French-Dutch by means of Orthographic Information and Cross-lingual Word Embeddings (2020.lrec-1)
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| Challenge: | Existing methods to identify cognate pairs in English-Dutch and French-Dutsch combine orthographic information with cross-lingual word embeddings. |
| Approach: | They combine traditional orthographic information with cross-lingual word embeddings to identify cognate pairs in English-Dutch and French-Dutsch. |
| Outcome: | The proposed classifier achieves good results on the basis of orthographic information but improves by including semantic information in the form of cross-lingual word embeddings. |