| Challenge: | Semantic specialization is not available in many languages because of their incomplete or non-existent structure. |
| Approach: | They propose a method that transfers specialization from a resource-rich source language to virtually any target language. |
| Outcome: | The proposed method performs lexical simplification, dialog state tracking, and textual similarity tasks in 5 languages. |
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Adversarial Propagation and Zero-Shot Cross-Lingual Transfer of Word Vector Specialization (D18-1)
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| Challenge: | Semantic specialization is a process of fine-tuning pre-trained distributional word vectors using external lexical knowledge to accentuate a particular semantic relation in the specialized vector space. |
| Approach: | They propose a method for specializing distributional word vectors using external lexical knowledge. |
| Outcome: | The proposed method improves on word similarity, dialog state tracking, and lexical simplification across three languages and on three tasks. |
Semantic Specialization of Distributional Word Vectors (D19-2)
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| Challenge: | Distributional word vectors conflate various paradigmatic and syntagmatic lexico-semantic relations. |
| Approach: | This tutorial provides an overview of specialization methods for distributional word vectors . a common solution is to include external lexico-semantic knowledge in a reshaped vector space . |
| Outcome: | This paper provides an overview of specialization methods for distributional word vectors . the most recent developments include a new method for asymmetric relations in Euclidean . |
Enhancing Word Embeddings with Knowledge Extracted from Lexical Resources (2020.acl-srw)
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| Challenge: | In this paper, we present an effective method for semantic specialization of word vector representations. |
| Approach: | They propose a method for semantic specialization of word vector representations using BabelNet. |
| Outcome: | The proposed method improves on word similarity and dialog state tracking tasks. |
Discriminating between Lexico-Semantic Relations with the Specialization Tensor Model (N18-2)
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| Challenge: | Existing methods to specialize distributional vectors to better reflect a particular relation are lacking in modern natural language processing. |
| Approach: | They propose a feed-forward neural architecture for discriminating between lexico-semantic relations . they propose to train relation classifiers using lexical relations from external resources . |
| Outcome: | The proposed model outperforms state-of-the-art models on two benchmark datasets and exhibits stable performance across languages. |
Massively Multilingual Lexical Specialization of Multilingual Transformers (2023.acl-long)
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| Challenge: | Existing work focused on lexical specialization of monolingual PLMs with immense quantities of monolinguistic constraints, but recent work shows that pretrained language models can be rewired to produce high-quality word representations and perform type-level lexicals. |
| Approach: | They propose to expose massively multilingual transformers to multilingual lexical knowledge at scale using BabelNet as a source of multilingual and cross-lingual type-level lexicon knowledge. |
| Outcome: | The proposed method shows that pretrained language models can be rewired to produce high-quality word representations and perform type-level lexical tasks. |
Explicit Retrofitting of Distributional Word Vectors (P18-1)
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| Challenge: | Existing models for word vector specialization focus on word co-occurrences from large text corpora, resulting in a tendency to fuse semantic similarity with other types of semantic relatedness. |
| Approach: | They propose to transform external lexico-semantic relations into training examples and learn an explicit retrofitting model. |
| Outcome: | The proposed model can specialize vector spaces of new languages and translate them to other languages. |
Lexical Resource Mapping via Translations (2022.lrec-1)
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| Challenge: | a lexical resource associates words with concepts in multiple languages, which makes it difficult to combine information from multiple resources. |
| Approach: | They propose a translation-based approach to mapping lexical resources . they use word-concept pairs to align WordNet/BabelNet to CLICS and OmegaWiki . |
| Outcome: | The proposed method achieves state-of-the-art accuracy without other sources of knowledge . it can be framed as word sense disambiguation, and it can improve on existing methods . |
Unifying Cross-Lingual Transfer across Scenarios of Resource Scarcity (2023.emnlp-main)
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| Challenge: | Existing approaches to deal with resource scarcity have not been developed to deal effectively with the problem. |
| Approach: | They propose to use a set of tools to harness data from one or more high-resource "source" languages to compensate for a shortage of data in low-resourced "target" languages. |
| Outcome: | The proposed technique can be easily adapted to unseen languages, extending the range of the proposed technique and translation-based transfer more broadly. |
Inferences for Lexical Semantic Resource Building with Less Supervision (2020.lrec-1)
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| Challenge: | lexical semantic resources may be built using various approaches such as extraction from corpora, integration of relevant pieces of knowledge from pre-existing knowledge resources and endogenous inference. |
| Approach: | They propose a method where the resource building process appears as a self learning process . they propose lexical and semantic resource building based on inference . |
| Outcome: | The proposed method reduces the human effort needed for lexical semantic resource building. |
Target Language-Aware Constrained Inference for Cross-lingual Dependency Parsing (D19-1)
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| Challenge: | Existing work on cross-lingual dependency parsing focuses on capturing commonalities between source and target languages and overlooking the potential to leverage the linguistic properties of the target languages to facilitate the transfer. |
| Approach: | They propose to use Lagrangian relaxation and posterior regularization techniques to conduct inference with corpus-statistics constraints to capture commonalities between source and target languages. |
| Outcome: | The proposed algorithms improve on 15 and 17 out of 19 target languages. |