Papers by Enrico Santus
Generalizing over Long Tail Concepts for Medical Term Normalization (2022.emnlp-main)
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Beatrice Portelli, Simone Scaboro, Enrico Santus, Hooman Sedghamiz, Emmanuele Chersoni, Giuseppe Serra
| Challenge: | Medical term normalization is a task of mapping a text to a large number of output classes. |
| Approach: | They propose a learning strategy that leverages hierarchical information to enhance generalizability of models. |
| Outcome: | The proposed strategy produces state-of-the-art performance on seen concepts and consistent improvements on unseen ones, allowing efficient zero-shot knowledge transfer across text typologies and datasets. |
Towards Debiasing Fact Verification Models (D19-1)
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Tal Schuster, Darsh Shah, Yun Jie Serene Yeo, Daniel Roberto Filizzola Ortiz, Enrico Santus, Regina Barzilay
| Challenge: | Prior research has shown that data collection methods that use crowdsourcing introduce idiosyncratic biases that impact performance in unexpected ways. |
| Approach: | They propose a method to regularize the training data to avoid idiosyncrasies in the datasets that are used for fact verification. |
| Outcome: | The proposed model outperforms the existing model on the FEVER dataset, achieving 61.7% of the baseline. |
BERT Prescriptions to Avoid Unwanted Headaches: A Comparison of Transformer Architectures for Adverse Drug Event Detection (2021.eacl-main)
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| Challenge: | Pretrained transformer-based models are a common choice for identifying drug events from social media texts. |
| Approach: | They propose to compare transformer-based models with in-domain language pretraining to find out which one is better at ADE detection. |
| Outcome: | The proposed models outperform SpanBERT and PubMedBERT on two benchmarks. |
Did the Cat Drink the Coffee? Challenging Transformers with Generalized Event Knowledge (2021.starsem-1)
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Paolo Pedinotti, Giulia Rambelli, Emmanuele Chersoni, Enrico Santus, Alessandro Lenci, Philippe Blache
| Challenge: | Prior work has explored the ability of computational models to predict word semantic fit with a given predicate. |
| Approach: | They compare Transformers Language Models to SDM to assess their performance . they found that TLMs do not capture important aspects of event knowledge . people can discriminate between typical and atypical events, they say . |
| Outcome: | The proposed models can achieve comparable performance to SDM, but they lack important aspects of event knowledge. |
SupCL-Seq: Supervised Contrastive Learning for Downstream Optimized Sequence Representations (2021.findings-emnlp)
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| Challenge: | SupCL-Seq extends contrastive learning from computer vision to sequence classification tasks. |
| Approach: | They propose a supervised alternative to Masked Language Modeling (MLM) that extends contrastive learning to sequence optimization in NLP by altering the dropout mask probability in standard Transformer architectures. |
| Outcome: | The proposed method leads to large gains on the GLUE benchmark, including 6% absolute improvement on CoLA, 5.4% on MRPC, 4.7% on RTE and 2.6% on STS-B. |
WorldCuisines: A Massive-Scale Benchmark for Multilingual and Multicultural Visual Question Answering on Global Cuisines (2025.naacl-long)
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Genta Indra Winata, Frederikus Hudi, Patrick Amadeus Irawan, David Anugraha, Rifki Afina Putri, Wang Yutong, Adam Nohejl, Ubaidillah Ariq Prathama, Nedjma Ousidhoum, Afifa Amriani, Anar Rzayev, Anirban Das, Ashmari Pramodya, Aulia Adila, Bryan Wilie, Candy Olivia Mawalim, Cheng Ching Lam, Daud Abolade, Emmanuele Chersoni, Enrico Santus, Fariz Ikhwantri, Garry Kuwanto, Hanyang Zhao, Haryo Akbarianto Wibowo, Holy Lovenia, Jan Christian Blaise Cruz, Jan Wira Gotama Putra, Junho Myung, Lucky Susanto, Maria Angelica Riera Machin, Marina Zhukova, Michael Anugraha, Muhammad Farid Adilazuarda, Natasha Christabelle Santosa, Peerat Limkonchotiwat, Raj Dabre, Rio Alexander Audino, Samuel Cahyawijaya, Shi-Xiong Zhang, Stephanie Yulia Salim, Yi Zhou, Yinxuan Gui, David Ifeoluwa Adelani, En-Shiun Annie Lee, Shogo Okada, Ayu Purwarianti, Alham Fikri Aji, Taro Watanabe, Derry Tanti Wijaya, Alice Oh, Chong-Wah Ngo
| Challenge: | Vision Language Models struggle with cultural-specific knowledge, especially in languages other than English and in underrepresented cultural contexts. |
| Approach: | They propose a visual question answering (VQA) dataset with text-image pairs across 30 languages and dialects and a training dataset. |
| Outcome: | The proposed model performs better with correct location context, but struggles with adversarial contexts and predicting specific regional cuisines and languages. |
Are Word Embeddings Really a Bad Fit for the Estimation of Thematic Fit? (2020.lrec-1)
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| Challenge: | In recent years, vectors derived from neural network training have replaced count-based distributional semantic models as a de facto standard for word representation in NLP. |
| Approach: | They propose to evaluate count models and word embeddings on thematic fit estimation by taking into account a larger number of parameters and verb roles and introducing dependency-based embedders in the comparison. |
| Outcome: | The proposed model outperforms count models and word embeddings in thematic fit estimation tasks while introducing dependency-based embedders. |
Deciphering Undersegmented Ancient Scripts Using Phonetic Prior (2021.tacl-1)
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| Challenge: | a number of undeciphered languages are still undecipherated, igniting fierce scientific debate . a recent study shows that NLP methods can successfully decipher lost languages . |
| Approach: | They propose a decipherment model that incorporates phonetic geometry into word segmentation and cognate alignment . they use the International Phonetic Alphabet to learn character embeddings based on historical sound change . |
| Outcome: | The proposed model shows that it can decipher both deciphered and undeciphered languages. |
A Rank-Based Similarity Metric for Word Embeddings (P18-2)
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| Challenge: | Word Embeddings have become a standard for word representations, with vector cosine being the only similarity metric. |
| Approach: | They propose to use rank-based similarity estimation metrics to measure word similarity . they find WE outperforms vector cosine in the recent outlier detection task . |
| Outcome: | The proposed rank-based measure outperforms vector cosine in the recent outlier detection task. |
IMaT: Unsupervised Text Attribute Transfer via Iterative Matching and Translation (D19-1)
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| Challenge: | Existing approaches to rewrite sentences with certain attributes are difficult and often result in poor content-preservation and ungrammaticality. |
| Approach: | They propose a method that uses a sequence-to-sequence model to learn attribute transfer . existing approaches try to explicitly disentangle content and attribute information . |
| Outcome: | The proposed method outperforms complex state-of-the-art systems by a large margin in sentiment modification and formality transfer tasks. |
GraphIE: A Graph-Based Framework for Information Extraction (N19-1)
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| Challenge: | Most modern Information Extraction (IE) systems are implemented as sequential taggers and model local dependencies. |
| Approach: | They propose a framework that operates over a graph representing a broad set of dependencies between textual units. |
| Outcome: | The proposed framework outperforms the state-of-the-art sequence tagging model on three different tasks. |
Exploring a Unified Sequence-To-Sequence Transformer for Medical Product Safety Monitoring in Social Media (2021.findings-emnlp)
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| Challenge: | Adverse Events (AEs) are harmful events resulting from the use of medical products. |
| Approach: | They propose a model that combines sequence-to-sequence learning with language transfer capabilities to improve model robustness. |
| Outcome: | The proposed approach achieves strong performance over baselines on English benchmarks. |