A Benchmark for Evaluating Lexical Semantic Change Detection Models and Their Components

核心概念

The LSCD Benchmark provides a standardized evaluation setup for models on lexical semantic change detection tasks, including the subtasks of Word-in-Context and Word Sense Induction, to enable reproducible results and facilitate model optimization.

要約

The LSCD Benchmark addresses the heterogeneity in modeling options and task definitions for lexical semantic change detection (LSCD), which makes it difficult to evaluate models under comparable conditions and reproduce results.

The benchmark exploits the modularity of the LSCD task, which can be broken down into three subtasks: 1) measuring semantic proximity between word usages (Word-in-Context), 2) clustering word usages based on semantic proximity (Word Sense Induction), and 3) estimating semantic change labels from the obtained clusterings.

The benchmark integrates a variety of LSCD datasets across 5 languages and diverse historical epochs, allowing for evaluation of WiC, WSI, and full LSCD pipelines. It provides transparent implementation and standardized evaluation procedures, enabling reproducible results and facilitating the development and optimization of LSCD models by allowing free combination of different model components.

The authors hope the LSCD Benchmark can serve as a starting point for researchers to improve LSCD models, by stimulating transfer between the fields of WiC, WSI and LSCD through the shared evaluation setup.

統計

The LSCD Benchmark integrates 15 LSCD datasets across 5 languages (German, English, Swedish, Spanish, Russian), with varying numbers of target words, POS distributions, usages per word, and human judgments.

引用

"The benchmark exploits the modularity of the meta task LSCD by allowing for evaluation of the subtasks WiC and WSI on the same datasets. It can be assumed that performance on the subtasks directly determines performance on the meta task."
"We hope that the resulting benchmark by standardizing the evaluation of LSCD models and providing models with near-SOTA performance can serve as a starting point for researchers to develop and improve models."

抽出されたキーインサイト

The LSCD Benchmark

by Dominik Schl... 場所 arxiv.org 04-02-2024

https://arxiv.org/pdf/2404.00176.pdf

深掘り質問

How can the LSCD Benchmark be extended to incorporate additional datasets or tasks beyond the current scope?

To extend the LSCD Benchmark, new datasets can be integrated by following a systematic approach. Firstly, identifying datasets that cover different languages, time periods, and domains can enhance the diversity and robustness of the benchmark. These datasets should be carefully curated to ensure high-quality annotations and relevance to the task at hand. Additionally, incorporating tasks beyond WiC, WSI, and LSCD, such as word sense disambiguation or semantic similarity tasks, can provide a more comprehensive evaluation of models' capabilities. By expanding the benchmark to include a wider range of datasets and tasks, researchers can gain a more holistic understanding of lexical semantic change detection and related NLP tasks.

What are the potential limitations or biases in the current set of datasets included in the benchmark, and how can these be addressed?

One potential limitation of the current datasets in the benchmark is the lack of diversity in terms of languages, time periods, and genres. This limitation can introduce biases in model evaluation and generalization. To address this, efforts should be made to include datasets from a more extensive range of languages and time periods, ensuring a more representative evaluation of models across different linguistic and temporal contexts. Additionally, biases related to annotation quality, dataset size, and task complexity should be carefully considered and mitigated through rigorous quality control measures, larger dataset sizes, and task-specific evaluation strategies. By addressing these limitations, the benchmark can provide a more comprehensive and unbiased evaluation platform for LSCD models.

How can the insights gained from evaluating WiC and WSI models within the LSCD Benchmark be leveraged to drive advances in other areas of natural language processing?

The insights gained from evaluating WiC and WSI models within the LSCD Benchmark can be leveraged to drive advances in other areas of natural language processing by facilitating knowledge transfer and model improvement. Firstly, the evaluation results can highlight the strengths and weaknesses of different model architectures, training strategies, and feature representations, which can inform the development of more robust and effective models for various NLP tasks. Secondly, the benchmark can serve as a testbed for exploring transfer learning techniques, domain adaptation methods, and multilingual model training, enabling researchers to leverage insights from LSCD tasks to enhance performance in related NLP domains. By leveraging the insights gained from the LSCD Benchmark, researchers can foster innovation and advancements in a wide range of NLP applications.