Papers with transformer-based encoders

3 papers
Sharing Encoder Representations across Languages, Domains and Tasks in Large-Scale Spoken Language Understanding (2023.acl-industry)

Copied to clipboard

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)

Copied to clipboard

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)

Copied to clipboard

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.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations