Time-Out: Temporal Referencing for Robust Modeling of Lexical Semantic Change (P19-1)
Copied to clipboard
| Challenge: | State-of-the-art lexical semantic change detection models suffer from noise stemming from vector space alignment. |
| Approach: | They propose a method to simulate lexical semantic change and control for possible biases by avoiding alignment. |
| Outcome: | The proposed method outperforms state-of-the-art models on a synthetic task and a manual testset. |
Similar Papers
Effects of Pre- and Post-Processing on type-based Embeddings in Lexical Semantic Change Detection (2021.eacl-main)
Copied to clipboard
| Challenge: | Lexical semantic change detection is a new and innovative research field. |
| Approach: | They propose to pre-train on large corpora and refine on diachronic target corpors to improve performance. |
| Outcome: | The proposed models improve on large corpora and diachronic target corpors . the proposed models are compared with existing models in a variety of learning scenarios . |
Diachronic word embeddings and semantic shifts: a survey (C18-1)
Copied to clipboard
| Challenge: | Existing methods for tracing time-related semantic shifts with word embedding models lack the cohesion, common terminology and shared practices of more established areas of natural language processing. |
| Approach: | They propose several axes along which these methods can be compared and propose a framework for comparison. |
| Outcome: | The proposed methods are compared with existing methods and outline their main challenges and potential applications. |
Improving Temporal Generalization of Pre-trained Language Models with Lexical Semantic Change (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods to improve neural language models perform poorly on emerging data. |
| Approach: | They propose a lexical-level masking strategy to post-train a neural language model using static data from past years. |
| Outcome: | The proposed method outperforms existing methods on two pre-trained language models, two classification tasks, and four benchmark datasets. |
A Wind of Change: Detecting and Evaluating Lexical Semantic Change across Times and Domains (P19-1)
Copied to clipboard
| Challenge: | Existing models for diachronic and synchronic detection of lexical semantic divergences are superficial and lack of comparison. |
| Approach: | They propose to extend benchmark models on a common state-of-the-art evaluation task . they also demonstrate that the same evaluation task and modelling approaches can be utilised for synchronic detection of domain-specific sense divergences in the field of term extraction. |
| Outcome: | The proposed model can be utilised for the detection of domain-specific sense divergences in the field of term extraction. |
Neural Temporality Adaptation for Document Classification: Diachronic Word Embeddings and Domain Adaptation Models (P19-1)
Copied to clipboard
| Challenge: | Recent studies show that document classifiers can become more stable over time when trained in ways that account for temporal variations. |
| Approach: | They propose a method for embedding diachronic word embedds into document classification models . they propose 'time-driven neural classification model' that accounts for temporal variations . |
| Outcome: | The proposed model can be trained on six corpora and make it more robust over time. |
Analysing Lexical Semantic Change with Contextualised Word Representations (2020.acl-main)
Copied to clipboard
| Challenge: | Existing studies on lexical semantic change have focused on detecting and characterising word meaning shifts using distributional semantic models. |
| Approach: | They propose a method that exploits the BERT neural language model to obtain representations of word usages, clusters these representations into usage types, and measures change along time with three proposed metrics. |
| Outcome: | The proposed method captures a variety of synchronic and diachronic linguistic phenomena and is highly reproducible and reproducible. |
Time Waits for No One! Analysis and Challenges of Temporal Misalignment (2022.naacl-main)
Copied to clipboard
| Challenge: | a pretrained model is optionally adapted through domain-specific pretraining, followed by task-specific finetuning. |
| Approach: | They establish a suite of eight tasks across different domains to quantify the effects of temporal misalignment in modern NLP systems. |
| Outcome: | The proposed tasks are based on eight domains and periods of time spanning five years or more and show that they have stronger effects than previous studies. |
Explaining and Improving BERT Performance on Lexical Semantic Change Detection (2021.eacl-srw)
Copied to clipboard
| Challenge: | Lexical semantic change detection is still a challenging field due to the success of type-based embeddings in SemEval-2020 Task 1 and other NLP tasks. |
| Approach: | They compare the performance of BERT embeddings with results from the word sense disambiguation dataset underlying SemEval-2020 Task 1 and the Italian follow-up task DIACR-Ita. |
| Outcome: | The proposed model outperforms token-based embeddings on lexical semantic change detection tasks. |
Temporal Referential Consistency: Do LLMs Favor Sequences Over Absolute Time References? (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing efforts to ensure temporal consistency in large language models are lacking in time-sensitive fields . temporal reasoning is essential for time- sensitive fields such as finance and healthcare . a new benchmark aims to improve temporal referent consistency of LLMs . |
| Approach: | They propose a temporal referential consistency benchmark with a resource TEMP-ReCon to assess LLMs across temporal references. |
| Outcome: | The proposed model improves LLMs' temporal consistency by comparing them to baseline models. |
Sequential Modelling of the Evolution of Word Representations for Semantic Change Detection (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing models that detect semantically shifted words do not account for its evolution through time. |
| Approach: | They propose three variants of sequential models for detecting semantically shifted words . they demonstrate that temporal modelling of word representations yields a clear-cut advantage . |
| Outcome: | The proposed models account for the changes in word representations over time. |