Papers with LSC
XL-LEXEME: WiC Pretrained Model for Cross-Lingual LEXical sEMantic changE (2023.acl-short)
Copied to clipboard
| Challenge: | Existing approaches to the Word in Context task use cross-encoders, which prevent the possibility of deriving comparable word embeddings. |
| Approach: | They propose a Lexical Semantic Change Detection model that extends SBERT, highlighting the target word in the sentence. |
| Outcome: | The proposed model outperforms the state-of-the-art on the multilingual benchmarks for SemEval-2020 Task 1 - Lexical Semantic Change (LSC) Detection and the RuShiftEval shared task. |
A Systematic Comparison of Contextualized Word Embeddings for Lexical Semantic Change (2024.naacl-long)
Copied to clipboard
| Challenge: | Contextualized embeddings are the preferred tool for modeling Lexical Semantic Change (LSC) current evaluations focus on a specific task known as Graded Change Detection (GCD) however, performance comparisons between different approaches are often misleading due to diverse settings. |
| Approach: | They evaluate the performance of contextualized embeddings for Lexical Semantic Change (LSC) they break the problem into Word-in-Context (WiC) and Word Sense Induction (WSI) tasks . |
| Outcome: | The proposed model outperforms other models on eight available benchmarks for Lexical Semantic Change (LSC) while comparable to GPT-4. |
LSC-Eval: A General Framework to Evaluate Methods for Assessing Dimensions of Lexical Semantic Change Using LLM-Generated Synthetic Data (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for measuring Lexical Semantic Change are lacking historical benchmarks. |
| Approach: | They propose a three-stage general-purpose evaluation framework that simulates theory-driven LSC using In-Context Learning and a lexical database. |
| Outcome: | The proposed framework evaluates the sensitivity of computational methods to synthetic change and their suitability for detecting change in specific dimensions and domains. |
Efficient Latent Semantic Clustering for Scaling Test-Time Computation of LLMs (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for scaling test-time computation rely on external models that introduce substantial computational overhead and fail to capture context-aware semantics. |
| Approach: | They propose a method that leverages the generator LLM’s internal hidden states for clustering, eliminating the need for external models. |
| Outcome: | The proposed method improves the computational efficiency of test-time scaling while maintaining or exceeding the performance of existing methods. |