Probing for Semantic Classes: Diagnosing the Meaning Content of Word Embeddings (P19-1)
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| Challenge: | Empirical analysis of word embeddings of ambiguous words is limited by the small size of manually annotated resources and by the fact that word senses are treated as unrelated individual concepts. |
| Approach: | They present a large dataset based on manual Wikipedia annotations and word senses, where word sense from different words are related by semantic classes. |
| Outcome: | The proposed method can predict whether a word is single-sense or multi-sensor, if the sense is frequent, and it can predict rare senses. |
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| Challenge: | Semantic embeddings have advanced the state of the art for natural language processing tasks . but their inner workings are poorly understood and there is a shortage of analysis tools . |
| Approach: | They propose to extend visual-semantic embeddings to multimodal domains by defining probing tasks for embeddable image-caption pairs and testing them with classifiers. |
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What Does This Word Mean? Explaining Contextualized Embeddings with Natural Language Definition (D19-1)
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| Challenge: | Contextualized word embeddings have boosted many NLP tasks compared with static word embeds. |
| Approach: | They propose a framework that can explain word meanings given contextualized word embeddings for better interpretation. |
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On the Correlation of Word Embedding Evaluation Metrics (2020.lrec-1)
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| Challenge: | Word embeddings are geometrical representations of word paradigmatics and syntagmatics. |
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Do Word Embeddings Capture Spelling Variation? (2020.coling-main)
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| Challenge: | Using word embeddings, we analyze spelling variation in word embeds trained on Twitter and Reddit data. |
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Modeling Personal Biases in Language Use by Inducing Personalized Word Embeddings (N19-1)
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| Challenge: | Existing studies have attempted to personalize models to improve performance on NLP tasks such as sentiment analysis but they did not estimate subjective input. |
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What you can cram into a single $&!#* vector: Probing sentence embeddings for linguistic properties (P18-1)
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| Challenge: | a lack of understanding of the properties of sentence embeddings is limiting the use of the techniques. |
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Together We Make Sense–Learning Meta-Sense Embeddings (2023.findings-acl)
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| Challenge: | Existing sense embeddings do not cover all senses of ambiguous words equally well due to discrepancies in their training resources. |
| Approach: | They propose a meta-sense embedding method that preserves sense neighbourhoods by combining multiple independently trained source sense embeddables. |
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Dynamic Contextualized Word Embeddings (2021.acl-long)
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| Challenge: | Static word embeddings that represent words by a single vector cannot capture word meaning in different linguistic and extralinguistic contexts. |
| Approach: | They propose dynamic contextualized word embeddings that represent words as a function of linguistic and extralinguistic contexts. |
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Sense Embeddings are also Biased – Evaluating Social Biases in Static and Contextualised Sense Embeddings (2022.acl-long)
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| Challenge: | Existing studies have evaluated social biases in word embeddings, but they are understudied. |
| Approach: | They propose to evaluate the social biases in sense embeddings using a benchmark dataset for word embedders. |
| Outcome: | The proposed measures show that even when no biases are found at word-level, there are still worrying levels of social biase at sense-level which are often ignored by the word- level bias evaluation measures. |
Towards Qualitative Word Embeddings Evaluation: Measuring Neighbors Variation (N18-4)
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| Challenge: | Using extrinsic evaluation methods, embeddings are evaluated on a specific task such as part-of-speech tagging or named-entity recognition. |
| Approach: | They propose a method to study the variation between word embeddings models trained with only one parameter by observing the distributional neighbors variation. |
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