Papers with manual feature engineering
Novelty Goes Deep. A Deep Neural Solution To Document Level Novelty Detection (C18-1)
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Tirthankar Ghosal, Vignesh Edithal, Asif Ekbal, Pushpak Bhattacharyya, George Tsatsaronis, Srinivasa Satya Sameer Kumar Chivukula
| Challenge: | Existing methods for document-level novelty detection are limited and do not require manual feature engineering. |
| Approach: | They propose a deep Convolutional Neural Networks based model to classify a document as novel or redundant on the basis of documents already seen by the system. |
| Outcome: | The proposed model outperforms the state-of-the-art on a document-level novelty detection dataset by a margin of 5% in terms of accuracy. |
EssayJudge: A Multi-Granular Benchmark for Assessing Automated Essay Scoring Capabilities of Multimodal Large Language Models (2025.findings-acl)
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Jiamin Su, Yibo Yan, Fangteng Fu, Zhang Han, Jingheng Ye, Xiang Liu, Jiahao Huo, Huiyu Zhou, Xuming Hu
| Challenge: | Automated Essay Scoring (AES) systems face three major challenges: reliance on handcrafted features that limit generalizability, difficulty in capturing fine-grained traits like coherence and argumentation, and inability to handle multimodal contexts. |
| Approach: | They propose a multimodal benchmark to evaluate AES capabilities across lexical-, sentence-, and discourse-level traits without manual feature engineering. |
| Outcome: | The proposed system can evaluate AES capabilities across lexical-, sentence-, and discourse-level traits without manual feature engineering. |
Revisiting Character-Based Neural Machine Translation with Capacity and Compression (D18-1)
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| Challenge: | Translating characters instead of words or word-fragments can simplify the processing pipeline but results in longer sequences . |
| Approach: | They propose to use sequence-to-sequence architectures of sufficient depth to solve the problem . they also evaluate the performance versus computation time tradeoffs they offer . |
| Outcome: | The proposed models outperform models operating over word fragments in character-level NMT, the authors show . they also show that the proposed models do not match the performance of their deep character baseline model . |
A strong baseline for question relevancy ranking (D18-1)
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| Challenge: | SemEval-16 and Semeval-17 community question answering shared tasks require complex pipelines and manual feature engineering to beat the IR baseline. |
| Approach: | They train a multi-task feed forward network on a bag of 14 distance measures for the input question pair and train it using language-independent features. |
| Outcome: | The proposed model outperforms the best shared task systems on the task of retrieving relevant previously asked questions. |
Comparing Feature-Engineering and Feature-Learning Approaches for Multilingual Translationese Classification (2021.emnlp-main)
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Daria Pylypenko, Kwabena Amponsah-Kaakyire, Koel Dutta Chowdhury, Josef van Genabith, Cristina España-Bonet
| Challenge: | Traditional hand-crafted features have been used for distinguishing between translated and original non-translated texts. |
| Approach: | They compare a feature-engineering-based approach to a features-learning-based one and use pre-trained neural word embeddings to train neural architectures. |
| Outcome: | The proposed approach outperforms other approaches by more than 20 accuracy points and the BERT-based model performs the best in both monolingual and multilingual settings. |