Papers with CA
Contrastive Attention for Automatic Chest X-ray Report Generation (2021.findings-acl)
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| Challenge: | Recent studies show that learning-based models fail to accurately capture and describe abnormal regions due to data bias. |
| Approach: | They propose a model that compares the current input image with normal images to capture abnormal regions by contrasting the input image and normal images. |
| Outcome: | The proposed model can be easily incorporated into existing models to boost their performance under most metrics. |
A System for Diacritizing Four Varieties of Arabic (D19-3)
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| Challenge: | Short vowels, aka diacritics, are omitted when writing different varieties of Arabic . diacritization is essential for language learning and text-to-speech applications . |
| Approach: | They propose a system for recovering diacritics in Arabic without short vowels . they use a character-based sequence-to-sequence deep learning model . |
| Outcome: | The proposed system beats all previous SOTA systems for Arabic varieties . it uses a character-based sequence-to-sequence deep learning model . |
Collaborative Data Exploration through Visualization: A Thesis Proposal Analyzing Impact of Conversational Assistants (2025.naacl-srw)
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| Challenge: | Data visualization is integral to any Exploratory Data Analysis task, but the steep learning curve and cognitive load of traditional tools excludes non-technical users. |
| Approach: | They propose to develop a conversational assistant capable of understanding user-initiated clarification requests and generating accurate responses to user requests. |
| Outcome: | The proposed conversational assistant can understand user-initiated clarification requests and generate accurate responses. |
Scientia Potentia Est—On the Role of Knowledge in Computational Argumentation (2022.tacl-1)
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| Challenge: | Existing research on argumentation models does not provide a systematic overview of the types of knowledge required in CA tasks. |
| Approach: | They propose a taxonomy of the types of knowledge required in CA tasks . authors propose exploitation of these knowledge types for four main research areas . |
| Outcome: | The proposed taxonomy proposes a systematic overview of the types of knowledge required in CA tasks. |
Enhancing Label Correlation Feedback in Multi-Label Text Classification via Multi-Task Learning (2021.findings-acl)
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| Challenge: | Existing approaches to multi-task learning fail to capture label correlations . Existing methods suffer from label order dependency, label combination over-fitting and error propagation problems. |
| Approach: | They propose a novel approach with multi-task learning to enhance label correlation feedback. |
| Outcome: | The proposed method outperforms baselines on AAPD and RCV1-V2 datasets. |
Automatic Section Recognition in Obituaries (2020.lrec-1)
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| Challenge: | Obituaries contain information about people’s values across times and cultures, which makes them useful for exploring cultural history. |
| Approach: | They propose to use a convolutional neural network to recognize these sections in obituaries to improve their annotation. |
| Outcome: | The proposed model outperforms bag-of-words and embedding-based BiLSTMs and BiLStm-CRFs with a micro F1 = 0.81. |
Tafsir Dataset: A Novel Multi-Task Benchmark for Named Entity Recognition and Topic Modeling in Classical Arabic Literature (2022.coling-1)
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Sajawel Ahmed, Rob van der Goot, Misbahur Rehman, Carl Kruse, Ömer Özsoy, Alexander Mehler, Gemma Roig
| Challenge: | Named entity recognition and topic modeling are crucial for downstream tasks in natural language processing. |
| Approach: | They propose to address named entity recognition and topic modeling on CA literature . they manually annotate the work of Tafsir Al-Tabari with span-based NEs . |
| Outcome: | The results show that the proposed task can perform state-of-the-art on historical topic models. |
IDC: Boost Text-to-image Retrieval via Indirect and Direct Connections (2024.lrec-main)
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| Challenge: | Dual Encoders (DE) and Cross Attention (CA) frameworks for image and text retrieval are more accurate but slower. |
| Approach: | They propose a dual encoders-based approach to map image and text inputs into a coordinated representation space and calculate their similarity directly. |
| Outcome: | Extensive experiments on the MSCOCO and Flickr30K datasets validate the effectiveness of the proposed methods. |