| 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. |
| Outcome: | The proposed method outperforms several baselines on Word Sense Disambiguation and Word-in-Context tasks. |
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Joint Learning of Sense and Word Embeddings (L18-1)
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| Challenge: | Existing methods for learning lower-dimensional representations of words using unlabelled data learn a single representation for a word, ignoring the different senses of that word (polysemy). |
| Approach: | They propose a method that jointly learns sense-aware word embeddings using both unlabelled and sense-tagged text corpora. |
| Outcome: | The proposed method outperforms competing methods on word similarity and short-text classification benchmark datasets. |
Learning Word Meta-Embeddings by Autoencoding (C18-1)
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| Challenge: | Existing word embeddings have shown superior performance in numerous Natural Language Processing (NLP) tasks, however, their performances vary significantly across different tasks. |
| Approach: | They propose to combine distributed word embeddings to produce more accurate and complete meta-embeddings of words. |
| Outcome: | The proposed meta-embeddings outperform the state-of-the-art in multiple tasks. |
With More Contexts Comes Better Performance: Contextualized Sense Embeddings for All-Round Word Sense Disambiguation (2020.emnlp-main)
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| Challenge: | Contextualized word embeddings have been used effectively across several tasks in Natural Language Processing, but it is difficult to link them to structured sources of knowledge. |
| Approach: | They propose a semi-supervised approach to producing sense embeddings for the lexical meanings within a lexicon that is comparable to that of contextualized word vectors. |
| Outcome: | The proposed approach outperforms state-of-the-art models in the English Word Sense Disambiguation task and in the multilingual one while training on sense-annotated data in English only. |
Multi-Sense Embeddings for Language Models and Knowledge Distillation (2025.findings-acl)
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| Challenge: | Transformer-based large language models generate different representations for the same token depending on context . however, words and tokens typically have only a limited number of senses . a knowledge distillation method can be used to learn a smaller student model . |
| Approach: | They propose a multi-sense embedding method that uses a clustering algorithm to generate a sense embeddable dictionary. |
| Outcome: | The proposed method offers significant space and inference time savings while maintaining competitive performance. |
Retrofitting Word Representations for Unsupervised Sense Aware Word Similarities (L18-1)
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| Challenge: | Standard word embeddings lack the ability to distinguish senses of a word by projecting them to exactly one vector. |
| Approach: | They propose to retrofit standard word embeddings to produce sense-aware embeddable vectors using external resources as sense inventories. |
| Outcome: | The proposed method improves word similarity and relatedness scores on multiple word embeddings and established word similarities, sometimes up to an impressive margin of +0.15 Spearman correlation score. |
Word Sense Disambiguation: Towards Interactive Context Exploitation from Both Word and Sense Perspectives (2021.acl-long)
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| Challenge: | Recent Word Sense Disambiguation systems have approached the upper bound of the task on standard evaluation benchmarks. |
| Approach: | They propose to convert the nearly isolated decisions into interrelated ones by exposing senses in context when learning sense embeddings in a similarity-based Sense Aware Context Exploitation architecture. |
| Outcome: | The proposed approach surpasses state-of-the-art on English and multilingual datasets by large margins. |
Learning Efficient Task-Specific Meta-Embeddings with Word Prisms (2020.coling-main)
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| Challenge: | Word embeddings possess different lexical properties depending on the notion of context defined at training time. |
| Approach: | They introduce a meta-embedding method that learns to combine source embeddings according to the task at hand. |
| Outcome: | The proposed method improves performance on six extrinsic evaluations over other methods. |
Constructing High Quality Sense-specific Corpus and Word Embedding via Unsupervised Elimination of Pseudo Multi-sense (L18-1)
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| Challenge: | Existing word embedding frameworks distinguish different senses of words by their contexts. |
| Approach: | They propose a framework for unsupervised corpus sense tagging which trains multi-sense word embeddings on a given corpus. |
| Outcome: | The proposed framework detects pseudo multi-senses without extra language resources without additional language resources. |
Enhancing the Context Representation in Similarity-based Word Sense Disambiguation (2021.emnlp-main)
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| Challenge: | Existing similarity-based systems focus on learning sense embeddings using only the sentence where the word appears, neglecting its global context. |
| Approach: | They propose a contextoriented embedding technique that takes better advantage of both word-level and sense-level global context of an ambiguous word for disambiguation. |
| Outcome: | The proposed method improves on all-words WSD benchmarks in knowledge-based category by large margins. |
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. |