Papers with text and
Semantic alignment in hyperbolic space for fine-grained emotion classification (2025.acl-srw)
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| Challenge: | Existing approaches to fine-grained emotion classification operate in Euclidean space, where the flat geometry makes it difficult to distinguish semantically similar label labels. |
| Approach: | They propose a semantic alignment framework that leverages the Lorentz model of hyperbolic space to embed text and label representations into hyperbolical space via the exponential map. |
| Outcome: | The proposed framework improves on two benchmark FEC datasets. |
Understanding the Modality Gap: An Empirical Study on the Speech-Text Alignment Mechanism of Large Speech Language Models (2025.emnlp-main)
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| Challenge: | LSLMs have impressive conversational generation abilities, but consistently fall short of traditional pipeline systems on semantic understanding benchmarks. |
| Approach: | They propose to analyze the performance gap between speech and text inputs through a systematic experiment . they find that representation similarity is strongly correlated with the modality gap . |
| Outcome: | The proposed models improve the accuracy of speech inputs and their semantic understanding benchmarks. |
Customizing Grapheme-to-Phoneme System for Non-Trivial Transcription Problems in Bangla Language (N19-1)
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Sudipta Saha Shubha, Nafis Sadeq, Shafayat Ahmed, Md. Nahidul Islam, Muhammad Abdullah Adnan, Md. Yasin Ali Khan, Mohammad Zuberul Islam
| Challenge: | Existing methods for Grapheme to phoneme conversion in Bangla language are mostly rule-based. |
| Approach: | They propose to use a lexicon to train a robust Grapheme to phoneme conversion system in Bangla language. |
| Outcome: | The proposed method outperforms other state-of-the-art approaches for G2P conversion in Bangla language. |
Learning to Model Multimodal Semantic Alignment for Story Visualization (2022.findings-emnlp)
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| Challenge: | Story visualization aims to generate sequence of images to narrate each sentence in a multi-sentence story . current methods face semantic misalignment because of their fixed architecture and diversity of input modalities . |
| Approach: | They propose to use a GAN-based generative model to match semantic levels between text and image representations to solve the semantic misalignment problem. |
| Outcome: | Experiments show that the proposed approach improves image quality and story consistency compared with state-of-the-art methods. |
Contrastive Learning for Task-Independent SpeechLLM-Pretraining (2025.findings-acl)
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| Challenge: | Large language models excel in speech processing tasks but their reliance on written text limits their application in real-world scenarios. |
| Approach: | They propose a task-independent speech pretraining stage and task-specific fine-tuning stage to adapt LLMs to speech processing tasks. |
| Outcome: | The proposed model outperforms models specialized on speech translation and question answering while being trained on 10% of the task-specific data. |
Contextualized Graph Representations for Generating Counter-Narratives against Hate Speech (2024.findings-emnlp)
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| Challenge: | Hate speech (HS) is a widespread problem in society with severe repercussions at both personal and societal levels. |
| Approach: | They propose to incorporate conversational history into CNs to confront biases and stereotypes driving hateful narratives. |
| Outcome: | The proposed strategies outperform existing methods on comparing graphical and text representations with varying degrees of context. |
Instances and Labels: Hierarchy-aware Joint Supervised Contrastive Learning for Hierarchical Multi-Label Text Classification (2023.findings-emnlp)
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| Challenge: | Existing approaches to hierarchical multi-label text classification (HMTC) ignore the correlation between similar samples and introduce noise . |
| Approach: | They propose a semi-supervised method that uses a label hierarchy to bring text and label embeddings closer to each other by supervised contrastive learning. |
| Outcome: | The proposed method bridges the gap between supervised contrastive learning and HMTC by bringing text and label embeddings closer. |
Towards Multilingual spoken Visual Question Answering system using Cross-Attention (2025.coling-main)
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| Challenge: | Visual question answering (VQA) is a multi-modal translation challenge that requires the analysis of both images and questions simultaneously to generate appropriate responses. |
| Approach: | They propose a textless multilingual visual question answering dataset that incorporates speech-based questions in English, german, spanish and french. |
| Outcome: | The proposed framework is superior to existing frameworks for speech-based VQA . the proposed framework can generate better results for image, text and audio representations . |
TLSA: LLM-Guided Text-Label Space Alignment with Contrastive Learning for Generalized Category Discovery (2026.acl-long)
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| Challenge: | Existing methods for generalized category discovery suffer from weak text–label alignment, inconsistent objectives across known and novel categories, and poor discrimination of semantically similar clusters. |
| Approach: | They propose a unified framework that enforces contrastive alignment between text and label representations within a shared semantic space. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on four benchmark datasets. |