Language Pivoting from Parallel Corpora for Word Sense Disambiguation of Historical Languages: A Case Study on Latin (2024.lrec-main)
Copied to clipboard
| Challenge: | Word Sense Disambiguation (WSD) is an important task in NLP . most of the work on this task has been done on contemporary English or other modern languages, leaving challenges posed by low-resource languages and diachronic change open. |
| Approach: | They propose to use existing bilingual corpora instead of native English datasets to generate a Latin WSD model. |
| Outcome: | The proposed approach achieves state-of-the-art on a standard benchmark for Latin WSD. |
Similar Papers
Sense-Annotated Corpora for Word Sense Disambiguation in Multiple Languages and Domains (2020.lrec-1)
Copied to clipboard
| Challenge: | Word Sense Disambiguation (WSD) is a field of NLP where data is usually tied to a specific language. |
| Approach: | They propose to release five large datasets annotated with word-senses in five different languages and 5 datasets in English for a different semantic domain. |
| Outcome: | The study shows that supervised models trained on the data achieve higher performance than those trained on other corpora. |
AMuSE-WSD: An All-in-one Multilingual System for Easy Word Sense Disambiguation (2021.emnlp-demo)
Copied to clipboard
| Challenge: | Word Sense Disambiguation (WSD) is a task of associating a word in context with its most appropriate sense from a predefined sense inventory. |
| Approach: | They propose to use a state-of-the-art neural model to integrate WSD into real-world applications. |
| Outcome: | The proposed system offers high-quality sense information in 40 languages through a state-of-the-art neural model for WSD. |
Multilingual Word Sense Disambiguation with Unified Sense Representation (2022.coling-1)
Copied to clipboard
| Challenge: | Existing researches on word sense disambiguation focus on English only. |
| Approach: | They propose to build knowledge and supervised based multilingual word sense disambiguation systems on a multilingual lexicon describing the same set of concepts across languages. |
| Outcome: | The proposed model can understand the fine-grained semantics of words under specific contexts. |
Improving Word Sense Disambiguation with Translations (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing WSD systems rarely consider multilingual information for word sense disambiguation (WSD). |
| Approach: | They propose a method that leverages multilingual information to improve a base WSD system by generating translations. |
| Outcome: | The proposed method improves performance of a base WSD system in English and multilingual WSD on several languages. |
Towards General-Domain Word Sense Disambiguation: Distilling Large Language Model into Compact Disambiguator (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for Word Sense Disambiguation rely heavily on manually annotated data, which limits coverage and generalization. |
| Approach: | They propose a framework that leverages large language models as knowledge distillers to build silver-standard WSD corpora by combining generation-based distillation and annotation-based disambiguation. |
| Outcome: | The proposed framework outperforms existing methods on general-domain benchmarks by 50% on the most challenging test set and by 1000 times fewer parameters. |
Word Sense Linking: Disambiguating Outside the Sandbox (2024.findings-acl)
Copied to clipboard
| Challenge: | Word Sense Disambiguation (WSD) systems have performed well on several evaluation benchmarks, but it still struggles to find downstream applications. |
| Approach: | They propose a task where systems have to identify which spans to disambiguate and link them to their most suitable meaning. |
| Outcome: | The proposed task performs above the estimated inter-annotator agreement on a set of words . the proposed system is based on 'transformer-based' architectures and iteratively relaxes the assumptions . |
Improved Word Sense Disambiguation Using Pre-Trained Contextualized Word Representations (D19-1)
Copied to clipboard
| Challenge: | Contextualized word representations are effective in downstream tasks such as question answering, named entity recognition, and sentiment analysis. |
| Approach: | They propose to integrate pre-trained contextualized word representations into a neural network that captures the whole sentence and the word representation in the sentence. |
| Outcome: | The proposed approach outperforms the state-of-the-art approach that makes use of non-contextualized word embeddings on multiple benchmark WSD datasets. |
Huge Automatically Extracted Training-Sets for Multilingual Word SenseDisambiguation (L18-1)
Copied to clipboard
| Challenge: | Word Sense Disambiguation is a crucial task in Natural Language Processing . supervised systems need to be trained on word-by-word basis, a problem that is beyond reach for resource-rich languages like English. |
| Approach: | They release six large-scale sense-annotated datasets in multiple languages to pave the way for supervised multilingual Word Sense Disambiguation. |
| Outcome: | The results show that large-scale sense annotations can be used as training sets for supervised systems. |
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. |
ConSeC: Word Sense Disambiguation as Continuous Sense Comprehension (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing systems for word Sense Disambiguation assume that each word can be disambiguated individually . a novel approach to WSD is proposed to address this limitation . |
| Approach: | They propose a supervised semantics-based approach to Word Sense Disambiguation that takes into account the senses assigned to nearby words. |
| Outcome: | The proposed approach surpasses all its competitors and sets a new state of the art on English WSD. |