Papers by Vladimir Araujo
Stress Test Evaluation of Transformer-based Models in Natural Language Understanding Tasks (2020.lrec-1)
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| Challenge: | Existing models are weak and take advantage of failures and errors in datasets to improve performance. |
| Approach: | They evaluate three Transformer-based models in Natural Language Inference and Question Answering tasks to see if they are more robust or have the same flaws as their predecessors. |
| Outcome: | The proposed models outperform recurrent neural network models to stress tests on both NLI and QA tasks. |
BRIGHTER: BRIdging the Gap in Human-Annotated Textual Emotion Recognition Datasets for 28 Languages (2025.acl-long)
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Shamsuddeen Hassan Muhammad, Nedjma Ousidhoum, Idris Abdulmumin, Jan Philip Wahle, Terry Ruas, Meriem Beloucif, Christine de Kock, Nirmal Surange, Daniela Teodorescu, Ibrahim Said Ahmad, David Ifeoluwa Adelani, Alham Fikri Aji, Felermino D. M. A. Ali, Ilseyar Alimova, Vladimir Araujo, Nikolay Babakov, Naomi Baes, Ana-Maria Bucur, Andiswa Bukula, Guanqun Cao, Rodrigo Tufiño, Rendi Chevi, Chiamaka Ijeoma Chukwuneke, Alexandra Ciobotaru, Daryna Dementieva, Murja Sani Gadanya, Robert Geislinger, Bela Gipp, Oumaima Hourrane, Oana Ignat, Falalu Ibrahim Lawan, Rooweither Mabuya, Rahmad Mahendra, Vukosi Marivate, Alexander Panchenko, Andrew Piper, Charles Henrique Porto Ferreira, Vitaly Protasov, Samuel Rutunda, Manish Shrivastava, Aura Cristina Udrea, Lilian Diana Awuor Wanzare, Sophie Wu, Florian Valentin Wunderlich, Hanif Muhammad Zhafran, Tianhui Zhang, Yi Zhou, Saif M. Mohammad
| Challenge: | Emotion recognition is an umbrella term for several NLP tasks, but most work on high-resource languages has focused on low-resourced languages. |
| Approach: | They propose to use emotion recognition to describe perceived emotions in 28 different languages and across several domains to identify and annotate the datasets. |
| Outcome: | The proposed datasets cover low-resource languages from Africa, Asia, Eastern Europe, and Latin America, with instances labeled by fluent speakers. |
Augmenting BERT-style Models with Predictive Coding to Improve Discourse-level Representations (2021.emnlp-main)
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| Challenge: | Existing language models do not produce suitable representations at the discourse level. |
| Approach: | They propose to augment BERT-style language models with a mechanism that allows them to learn suitable discourse-level representations by incorporating top-down connections that operate at the intermediate layers of the network. |
| Outcome: | The proposed approach improves in 6 out of 11 tasks by detecting discourse relationship detection. |
Pixology: Probing the Linguistic and Visual Capabilities of Pixel-based Language Models (2024.emnlp-main)
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| Challenge: | PIXEL is a vision transformer that has been pre-trained on rendered text . however, it is not able to outperform monolingual subwords like BERT . |
| Approach: | They propose to use PIXEL as a vision transformer to train on rendered text to explore the gap between its visual and linguistic understanding. |
| Outcome: | The proposed model outperforms monolingual subword models in most other contexts, but it lacks the linguistic knowledge to perform in language tasks. |
Learning to Route for Dynamic Adapter Composition in Continual Learning with Language Models (2024.findings-emnlp)
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| Challenge: | Recent work shows that PEFT methods can be competitive with, or even superior to, full fine-tuning of PLMs. |
| Approach: | They propose a method that isolates the training of new PEFT modules to ensure their task specialization and learns to compose them by training a network of routers that leverages a small memory containing examples of previously seen tasks. |
| Outcome: | The proposed method improves generalization and performance in two CL setups. |
A Memory Model for Question Answering from Streaming Data Supported by Rehearsal and Anticipation of Coreference Information (2023.findings-acl)
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| Challenge: | Existing question answering methods assume that the input content can always be accessed while answering the question. |
| Approach: | They propose a model that performs rehearsal and anticipation while processing inputs to memorize important information for question answering tasks from streaming data. |
| Outcome: | The proposed model improves on short-sequence (bAbI) and large-squence textual (NarrativeQA) and video (ActivityNet-QA) question answering datasets. |
SemRel2024: A Collection of Semantic Textual Relatedness Datasets for 13 Languages (2024.findings-acl)
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Nedjma Ousidhoum, Shamsuddeen Muhammad, Mohamed Abdalla, Idris Abdulmumin, Ibrahim Ahmad, Sanchit Ahuja, Alham Aji, Vladimir Araujo, Abinew Ayele, Pavan Baswani, Meriem Beloucif, Chris Biemann, Sofia Bourhim, Christine Kock, Genet Dekebo, Oumaima Hourrane, Gopichand Kanumolu, Lokesh Madasu, Samuel Rutunda, Manish Shrivastava, Thamar Solorio, Nirmal Surange, Hailegnaw Tilaye, Krishnapriya Vishnubhotla, Genta Winata, Seid Yimam, Saif Mohammad
| Challenge: | SemRel datasets are annotated by native speakers across 13 languages . they are used to characterise the relationship between two units of text . |
| Approach: | They propose to use a semantic relatedness dataset to measure the degree of semantic textual relatedness between sentences in Afrikaans, Algerian Arabic, Amharic, English, Hausa, Hindi, Indonesian, Kinyarwanda, Marathi, Moroccan Arabic, Modern Standard Arabic, Spanish, and Telugu. |
| Outcome: | The proposed datasets are annotated by native speakers across 13 languages and represent the semantic relatedness of 13 languages. |
Evaluation Benchmarks for Spanish Sentence Representations (2022.lrec-1)
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Vladimir Araujo, Andrés Carvallo, Souvik Kundu, José Cañete, Marcelo Mendoza, Robert E. Mercer, Felipe Bravo-Marquez, Marie-Francine Moens, Alvaro Soto
| Challenge: | Existing and newly constructed datasets address different tasks from various domains. |
| Approach: | They propose to use Spanish SentEval and Spanish DiscoEval to evaluate stand-alone and discourse-aware sentence representations. |
| Outcome: | The proposed benchmarks evaluate the capabilities of stand-alone and discourse-aware sentence representations in Spanish and show that they are more robust and comparable than previous benchmarks. |
CaMMT: Benchmarking Culturally Aware Multimodal Machine Translation (2025.findings-emnlp)
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Emilio Villa-Cueva, Sholpan Bolatzhanova, Diana Turmakhan, Kareem Elzeky, Henok Biadglign Ademtew, Alham Fikri Aji, Vladimir Araujo, Israel Abebe Azime, Jinheon Baek, Frederico Belcavello, Fermin Cristobal, Jan Christian Blaise Cruz, Mary Dabre, Raj Dabre, Toqeer Ehsan, Naome A Etori, Fauzan Farooqui, Jiahui Geng, Guido Ivetta, Thanmay Jayakumar, Soyeong Jeong, Zheng Wei Lim, Aishik Mandal, Sofía Martinelli, Mihail Minkov Mihaylov, Daniil Orel, Aniket Pramanick, Sukannya Purkayastha, Israfel Salazar, Haiyue Song, Tiago Timponi Torrent, Debela Desalegn Yadeta, Injy Hamed, Atnafu Lambebo Tonja, Thamar Solorio
| Challenge: | a human-curated benchmark of over 5,800 triples of images is used to evaluate multimodal translation systems. |
| Approach: | They introduce a human-curated benchmark of over 5,800 triples of images along with parallel captions in English and regional languages. |
| Outcome: | The results show that visual context improves translation quality in culturally-specific items . |
ALBETO and DistilBETO: Lightweight Spanish Language Models (2022.lrec-1)
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| Challenge: | Recent advances in pre-trained language models have made them more popular . however, there are still limited versions of these models for other languages . |
| Approach: | They present ALBETO and DistilBETO which are versions of ALBERT and DistillBERT pre-trained exclusively on Spanish corpora. |
| Outcome: | The proposed models outperform BETO and ALBERT on Spanish datasets . the models outpace BETO on MLDoc, PAWS-X, XNLI, MLQA, SQAC and XQuAD datasets. |
Extracting and Encoding: Leveraging Large Language Models and Medical Knowledge to Enhance Radiological Text Representation (2024.findings-acl)
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| Challenge: | Advancing representation learning in specialized fields like medicine remains challenging due to the scarcity of expert annotations for text and images. |
| Approach: | They propose a Fact Extractor that leverages large language models to extract factual statements from radiology reports. |
| Outcome: | The proposed framework outperforms current state-of-the-art methods in sentence ranking, natural language inference, and label extraction tasks. |
Sequence-to-Sequence Spanish Pre-trained Language Models (2024.lrec-main)
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| Challenge: | Spanish language models have demonstrated proficiency in natural language understanding and generation, but there is a scarcity of encoder-decoder models specifically designed for sequence-to-sequence tasks. |
| Approach: | They propose to implement encoder-decoder architectures pre-trained on Spanish corpora . they use them to assess sequence-to-sequence tasks including summarization, question answering . |
| Outcome: | The proposed models outperform models on sequence-to-sequence tasks in Spanish . the models show that they perform well across all tasks, the authors note . |
How Relevant is Selective Memory Population in Lifelong Language Learning? (2022.aacl-short)
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| Challenge: | Existing approaches to lifelong language learning rely on sparse experience replay to prevent catastrophic forgetting. |
| Approach: | They propose to use a selective memory population to store a uniform number of samples from the entire data stream to improve model performance. |
| Outcome: | The proposed methods show that they are relevant for lifelong language learning tasks, especially for low memory size, and consistent with computer vision studies. |