| Challenge: | Compositional Distributional Semantic Models (CDSMs) model the meaning of phrases and sentences in vector space. |
| Approach: | They propose to use lexical substitution to evaluate CDSMs by comparing a LexSub-annotated corpus with a manual LexSub annotation. |
| Outcome: | The proposed model outperforms simple component-wise CDSMs and performs on par with the context2vec LexSub model using the same context. |
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LexSubCon: Integrating Knowledge from Lexical Resources into Contextual Embeddings for Lexical Substitution (2022.acl-long)
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| Challenge: | Lexical substitution is the task of generating meaningful substitutes for a word in a given textual context. |
| Approach: | They propose an end-to-end lexical substitution framework based on contextual embedding models that can identify highly-accurate substitute candidates. |
| Outcome: | The proposed framework outperforms state-of-the-art embedding models on LS07 and CoInCo benchmark datasets by at least 2% over existing embeddable models. |
Learning Lexical Subspaces in a Distributional Vector Space (2020.tacl-1)
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| Challenge: | Existing word embeddings that can cluster distributionally related words are weak, but they can be used to cluster words that might not be semantically similar. |
| Approach: | They propose a framework that injects lexical-semantic relations into distributional word embeddings by defining subspaces of the distributional vector space in which a lexically related relation should hold. |
| Outcome: | The proposed framework outperforms existing systems on relatedness and hypernymy tasks while being competitive on word similarity tasks. |
Contextualized context2vec (D19-55)
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| Challenge: | Lexical substitution ranks substitution candidates from the viewpoint of paraphrasability for a target word in a given sentence. |
| Approach: | They propose a method that combines two approaches to contextualize word embeddings for lexical substitution. |
| Outcome: | The proposed method outperforms the current state-of-the-art method and assigns English proficiency levels to all target words and substitution candidates. |
LexSym: Compositionality as Lexical Symmetry (2023.acl-long)
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| Challenge: | Existing approaches to generalize compositional models fail to generalise from small datasets. |
| Approach: | They propose a domain-general and model-agnostic formulation of compositionality as a constraint on symmetries of data distributions rather than models. |
| Outcome: | The proposed procedure matches or surpasses state-of-the-art, task-specific models on COGS semantic parsing, SCAN and Alchemy instruction following, and CLEVR-CoGenT visual question answering datasets. |
CILex: An Investigation of Context Information for Lexical Substitution Methods (2022.coling-1)
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| Challenge: | Existing methods for lexical substitution rely on manually curated lexicals and contextual word embedding models. |
| Approach: | They propose a method that uses contextual sentence embeddings to generate substitutes for a target word given a context and a model that captures additional context information complimenting contextual word embedders. |
| Outcome: | The proposed method is state-of-the-art on the widely used LS07 and CoInCo datasets with P@1 scores of 55.96% and 57.25% for lexical substitution. |
Always Keep your Target in Mind: Studying Semantics and Improving Performance of Neural Lexical Substitution (2020.coling-main)
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| Challenge: | Lexical substitution is a powerful technology used in various NLP applications . it generates plausible words that can replace a given word in a textual context . |
| Approach: | They propose to use a large-scale comparative study to compare lexical substitution methods . they compare existing and new methods using word sense induction datasets . |
| Outcome: | The proposed methods improve competitive results by incorporating information about the target word into the models. |
Evaluating Lexical Proficiency in Neural Language Models (2025.acl-long)
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| Challenge: | Recent advances in Natural Language Processing have been significantly shaped by the Deep Learning tsunami and the introduction of Transformer-based Language Models. |
| Approach: | They validated a framework to assess the lexical proficiency and linguistic creativity of Transformer-based Language Models (LMs) by analyzing performance of LMs of different sizes across tasks involving the generation, definition, and contextual usage of lexicals, neologisms, and nonce words. |
| Outcome: | The framework evaluates LMs in mono- and multilingual configuration across tasks involving the generation, definition, and contextual usage of lexicalized words, neologisms, and nonce words. |
Unsupervised Lexical Substitution with Decontextualised Embeddings (2022.coling-1)
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| Challenge: | Existing methods for lexical substitution using pre-trained language models have some limitations. |
| Approach: | They propose an unsupervised method for lexical substitution using pre-trained language models. |
| Outcome: | The proposed method outperforms baseline models and establishes a state-of-the-art without supervision or fine-tuning. |
Analysing Lexical Semantic Change with Contextualised Word Representations (2020.acl-main)
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| Challenge: | Existing studies on lexical semantic change have focused on detecting and characterising word meaning shifts using distributional semantic models. |
| Approach: | They propose a method that exploits the BERT neural language model to obtain representations of word usages, clusters these representations into usage types, and measures change along time with three proposed metrics. |
| Outcome: | The proposed method captures a variety of synchronic and diachronic linguistic phenomena and is highly reproducible and reproducible. |
Compositional Generalization and Natural Language Variation: Can a Semantic Parsing Approach Handle Both? (2021.acl-long)
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| Challenge: | Existing approaches to semantic parsing only evaluated on synthetic datasets that are not representative of natural language variation. |
| Approach: | They propose a semantic parsing approach that handles both natural language variation and compositional generalization. |
| Outcome: | The proposed model outperforms existing models across compositional generalization challenges on non-synthetic datasets while being competitive with the state-of-the-art on standard evaluations. |