Word Embedding Evaluation Datasets and Wikipedia Title Embedding for Chinese (L18-1)
Copied to clipboard
| Challenge: | Existing evaluation sets for word embeddings in English are limited. |
| Approach: | They propose to translate existing evaluation sets from English to Chinese to evaluate Chinese word embeddings. |
| Outcome: | The proposed evaluation sets are based on translations of popular evaluation sets from English to Chinese and human rating from Amazon Mechanical Turk workers. |
Similar Papers
On the Correlation of Word Embedding Evaluation Metrics (2020.lrec-1)
Copied to clipboard
| Challenge: | Word embeddings are geometrical representations of word paradigmatics and syntagmatics. |
| Approach: | They propose to investigate evaluation metrics on various datasets to find correlations . they propose a fast solution to select the best word embeddings among many others . |
| Outcome: | The proposed method could be used to select the best word embeddings among many others. |
Exploring the Value of Personalized Word Embeddings (2020.coling-main)
Copied to clipboard
| Challenge: | a subset of words belonging to specific psycholinguistic categories vary more in their representations across users . combining generic and personalized word embeddings yields the best performance . |
| Approach: | They propose personalized word embeddings and compare their performance to generic ones . they show that personalized word representations can be leveraged for improved performance . |
| Outcome: | The proposed model can be used for authorship attribution. |
A Deeper Look into Dependency-Based Word Embeddings (N18-4)
Copied to clipboard
| Challenge: | Word embeddings trained with dependency contexts excel at different tasks, and enhanced dependencies often improve performance. |
| Approach: | They propose to use dependency-based word embeddings to capture semantic similarity rather than relatedness. |
| Outcome: | The results show that word embeddings trained with Universal and Stanford dependencies excel at different tasks and that enhanced dependencies often improve performance. |
Domain-Specific Word Embeddings with Structure Prediction (2023.tacl-1)
Copied to clipboard
| Challenge: | Current word embedding methods do not provide a way to use or predict information on structure between sub-corpora, time or domain. |
| Approach: | They propose a word embedding method that provides general word representations for the whole corpus, domain-specific representations and embeddable alignment simultaneously. |
| Outcome: | The proposed method provides better performance than baselines on a dataset of science and philosophy articles. |
Just Rank: Rethinking Evaluation with Word and Sentence Similarities (2022.acl-long)
Copied to clipboard
| Challenge: | Word and sentence similarity tasks are the de facto evaluation method for embeddings. |
| Approach: | They propose a new intrinsic evaluation method called EvalRank which shows a much stronger correlation with downstream tasks. |
| Outcome: | The proposed method shows a much stronger correlation with downstream tasks and is released for future benchmarking purposes. |
Evaluating bilingual word embeddings on the long tail (N18-2)
Copied to clipboard
| Challenge: | Bilingual word embeddings are useful for bilingual lexicon induction, but they focus on frequent words in general domains. |
| Approach: | They propose to evaluate bilingual word embeddings on rare words in different domains . they propose to use a multilingual dataset to build and combine BWEs based on a single word . |
| Outcome: | The proposed evaluations show that state-of-the-art methods fail on rare words . the proposed evaluation is based on a gold standard dataset and code . |
On Evaluation of Bangla Word Analogies (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing word embeddings in Bangla struggle to perform well on low-resource data sets. |
| Approach: | They propose to use a benchmark dataset of Bangla word analogies to evaluate the quality of existing Bangla embeddings. |
| Outcome: | The proposed evaluation set includes 16,678 unique word analogies in Bangla and a translated and curated version of the original Mikolov dataset (10,594 samples) . |
Benchmarking Meta-embeddings: What Works and What Does Not (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods to build meta-embeddings have been evaluated using a variety of methods and datasets, which makes it difficult to draw meaningful conclusions regarding the merits of each approach. |
| Approach: | They propose a unified framework for a fair and objective meta-embedding evaluation using intrinsic and extrinsic tasks. |
| Outcome: | The proposed framework outperforms existing methods on intrinsic and extrinsic evaluation benchmarks and outperformed existing methods. |
Are Word Embeddings Really a Bad Fit for the Estimation of Thematic Fit? (2020.lrec-1)
Copied to clipboard
| Challenge: | In recent years, vectors derived from neural network training have replaced count-based distributional semantic models as a de facto standard for word representation in NLP. |
| Approach: | They propose to evaluate count models and word embeddings on thematic fit estimation by taking into account a larger number of parameters and verb roles and introducing dependency-based embedders in the comparison. |
| Outcome: | The proposed model outperforms count models and word embeddings in thematic fit estimation tasks while introducing dependency-based embedders. |
Improving Cross-Domain Chinese Word Segmentation with Word Embeddings (N19-1)
Copied to clipboard
| Challenge: | Existing approaches to Chinese word segmentation (CWS) are character-based and word-based . character-driven approaches use conditional random field models to label sequences, with complex hand-crafted discrete features. |
| Approach: | They propose a semi-supervised word-based approach to improve cross-domain Chinese word segmentation given a baseline segmenter. |
| Outcome: | The proposed model outperforms state-of-the-art approaches on five datasets covering domains in novels, medicine, and patent. |