Papers with transformer-based encoders
Sharing Encoder Representations across Languages, Domains and Tasks in Large-Scale Spoken Language Understanding (2023.acl-industry)
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Jonathan Hueser, Judith Gaspers, Thomas Gueudre, Chandana Prakash, Jin Cao, Daniil Sorokin, Quynh Do, Nicolas Anastassacos, Tobias Falke, Turan Gojayev
| Challenge: | Larger encoders can improve accuracy for spoken language understanding (SLU) but are difficult to use given the inference latency constraints of online systems. |
| Approach: | They propose to use a larger 170M parameter BERT encoder that shares representations across languages, domains and tasks for SLU. |
| Outcome: | The proposed encoders achieve state-of-the-art performance on numerous NLP tasks. |
Unveiling Dual Quality in Product Reviews: An NLP-Based Approach (2025.acl-industry)
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| Challenge: | Dual quality is a problem where products with identical ingredients or characteristics are sold under the same brand and similar packaging in different markets, but are significantly altered in composition or quality parameters. |
| Approach: | They propose to use natural language processing to detect inconsistent product quality by analyzing a Polish-language dataset and using different approaches. |
| Outcome: | The proposed approach can detect and address inconsistent product quality in Polish and other languages. |
Detecting Legal Citations in United Kingdom Court Judgments (2025.emnlp-main)
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| Challenge: | citation detection in court judgments is challenging because of the complexity of legal language . citation analysis is critical for many legal applications, but the complexity is not always easy to solve. |
| Approach: | They compare three different models for citation detection in court judgments using the Cambridge Law Corpus . they compare rulebased regular expressions, transformer-based encoders and large language models . |
| Outcome: | The proposed model outperforms the existing models in the citation analysis and analysis of 190 court judgments. |