Meta-Learning with Variational Semantic Memory for Word Sense Disambiguation (2021.acl-long)
Copied to clipboard
| Challenge: | Existing methods for word sense disambiguation (WSD) lack large annotated datasets with sufficient coverage of words . performance of such methods lags behind fully-supervised methods . a meta-learning model is proposed to solve this problem . |
| Approach: | They propose a model of semantic memory for supervised word sense disambiguation using meta-learning. |
| Outcome: | The proposed model improves performance in few-shot WSD and produces meaning prototypes that capture similar senses of distinct words. |
Similar Papers
Learning to Learn to Disambiguate: Meta-Learning for Few-Shot Word Sense Disambiguation (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for word sense disambiguation (WSD) are limited and require large datasets annotated with word senses. |
| Approach: | They propose a meta-learning framework for few-shot word sense disambiguation where the goal is to learn to disambiguate unseen words from only a few labeled instances. |
| Outcome: | The proposed framework is based on a large training dataset and a small number of examples. |
FEWS: Large-Scale, Low-Shot Word Sense Disambiguation with the Dictionary (2021.eacl-main)
Copied to clipboard
| Challenge: | Existing models for Word Sense Disambiguation struggle to disambiguate rare senses . current models struggle to learn senses with few training examples . |
| Approach: | They introduce a low-shot WSD dataset automatically extracted from example sentences in Wiktionary. |
| Outcome: | The proposed dataset outperforms baseline models on rare senses in existing datasets. |
Enhancing Modern Supervised Word Sense Disambiguation Models by Semantic Lexical Resources (L18-1)
Copied to clipboard
| Challenge: | Existing supervised models for Word Sense Disambiguation (WSD) are limited to knowledge-based approaches. |
| Approach: | They propose to use WordNet and WordNet Domains to enhance supervised WSD models by introducing semantic features into the classifiers and using the SLR structure to augment training data. |
| Outcome: | The proposed model improves the state-of-the-art in Word Sense Disambiguation (WSD) The proposed approach is compared with the state of the art in the most popular benchmarks. |
Together We Make Sense–Learning Meta-Sense Embeddings (2023.findings-acl)
Copied to clipboard
| 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. |
Semantic Specialization for Knowledge-based Word Sense Disambiguation (2023.eacl-main)
Copied to clipboard
| Challenge: | Existing methods for knowledge-based Word Sense Disambiguation (WSD) use only lexical knowledge to adapt contextualized embeddings. |
| Approach: | They propose a semantic specialization where contextualized embeddings are adapted to the WSD task using only lexical knowledge. |
| Outcome: | The proposed method outperforms previous studies that adapt contextualized embeddings while controlling deviations from the original embeddables. |
Improved Word Sense Disambiguation with Enhanced Sense Representations (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing supervised word sense disambiguation systems do not provide enough information about word senses. |
| Approach: | They propose to incorporate synonyms, example phrases or sentences showing usage of word senses and sense gloss of hypernyms into the sense representations. |
| Outcome: | The proposed system achieves an F1 score of 82.0% on the standard benchmark test dataset of the English all-words WSD task. |
One Classifier for All Ambiguous Words: Overcoming Data Sparsity by Utilizing Sense Correlations Across Words (2020.lrec-1)
Copied to clipboard
| Challenge: | Existing word-specific classifiers lack the ability to generalize across words and require limited sense-annotated data for every word. |
| Approach: | They propose to learn a single model that derives sense representations and enforces congruence between a word instance and its right sense by using both sense-annotated data and lexical resources. |
| Outcome: | Empirical evaluation shows the proposed model outperforms classifier-based models by 1.7%, 2.5% and 3.8% in F1-score on GloVe, ELMo and BERT word embeddings respectively. |
Word Sense Disambiguation: Towards Interactive Context Exploitation from Both Word and Sense Perspectives (2021.acl-long)
Copied to clipboard
| 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. |
Enhancing the Context Representation in Similarity-based Word Sense Disambiguation (2021.emnlp-main)
Copied to clipboard
| 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. |
Non-Parametric Few-Shot Learning for Word Sense Disambiguation (2021.naacl-main)
Copied to clipboard
| Challenge: | Word sense disambiguation (WSD) is a problem in natural language processing . 84% of annotated words have less than 10 examples in the long-tail distribution . |
| Approach: | They propose a non-parametric few-shot learning approach to mitigate word sense disambiguation . they use a metric space to compute distances among the senses of a given word . |
| Outcome: | The proposed method achieves a 75.1 F1 score on the unified evaluation benchmark. |