| Challenge: | Technical support problems are long and complex and cannot be correctly parsed by tools designed for natural language. |
| Approach: | They propose a sequence labelling task and a supervised text segmentation approach to solve this problem. |
| Outcome: | The proposed approach improves on the downstream task of answer retrieval. |
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| Challenge: | Towards language scalability, major progress has been achieved in multilingual language technology in recent years. |
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Text Segmentation as a Supervised Learning Task (N18-2)
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| Challenge: | Existing datasets for text segmentation are small in size and do not represent the natural distribution of text in documents. |
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Interpretable Text Embeddings and Text Similarity Explanation: A Survey (2025.emnlp-main)
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| Challenge: | Text embeddings are a fundamental component in many NLP tasks, but their interpretation and explanation remain challenging. |
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Automatic Annotation of Semantic Term Types in the Complete ACL Anthology Reference Corpus (L18-1)
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| Challenge: | a recent increase in quantitative studies of scientific text collections has led to a significant increase in the use of semantic labeling techniques. |
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How Well Do Text Embedding Models Understand Syntax? (2023.findings-emnlp)
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Improving Generalization in Language Model-based Text-to-SQL Semantic Parsing: Two Simple Semantic Boundary-based Techniques (2023.acl-short)
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Hierarchical Bracketing Encodings Work for Dependency Graphs (2025.emnlp-main)
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| Challenge: | Sequence labeling (SL) is a simple yet effective paradigm for a wide range of natural language problems. |
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Recent Trends in Linear Text Segmentation: A Survey (2024.findings-emnlp)
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More Embeddings, Better Sequence Labelers? (2020.findings-emnlp)
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Viable Dependency Parsing as Sequence Labeling (N19-1)
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