Sõnajaht: Definition Embeddings and Semantic Search for Reverse Dictionary Creation (2024.starsem-1)
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| Challenge: | Existing systems that use exact term matching to find words are based on information retrieval. |
| Approach: | They propose to use pre-trained language models and approximate nearest neighbors search algorithms to enhance and enrich an Estonian lexicon resource by introducing cross-lingual reverse dictionary functionality powered by semantic search. |
| Outcome: | The proposed system produces a 1 and 2 routs in the monolingual and cross-lingual settings using the unlabeled evaluation approach. |
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| Challenge: | Existing methods for learning cross-lingual word embeddings incorporate sub-word information during training. |
| Approach: | They propose a method that incorporates sub-word information during training to learn cross-lingual word embeddings from monolingual data and a bilingual lexicon. |
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Evaluating a Joint Training Approach for Learning Cross-lingual Embeddings with Sub-word Information without Parallel Corpora on Lower-resource Languages (2021.starsem-1)
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| Challenge: | Cross-lingual word embeddings provide a way for information to be transferred between languages. |
| Approach: | They propose a joint training approach that incorporates sub-word information during training to learn cross-lingual embeddings. |
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Learning Embeddings for Rare Words Leveraging Internet Search Engine and Spatial Location Relationships (2021.starsem-1)
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| Challenge: | Existing word embedding techniques depend heavily on the frequencies of words in the corpus, and fail to provide reliable representations for rare words. |
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Inducing Language-Agnostic Multilingual Representations (2021.starsem-1)
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| Challenge: | Cross-lingual representations have the potential to make NLP techniques available to the vast majority of languages in the world, but they currently require large pretraining corpora or access to typologically similar languages. |
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Assessing Polyseme Sense Similarity through Co-predication Acceptability and Contextualised Embedding Distance (2020.starsem-1)
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| Challenge: | Co-predication is a commonly used linguistic test to tell apart shifts in polysemic sense from changes in homonymic meaning. |
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A Study on Using Semantic Word Associations to Predict the Success of a Novel (2021.starsem-1)
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Syeda Jannatus Saba, Biddut Sarker Bijoy, Henry Gorelick, Sabir Ismail, Md Saiful Islam, Mohammad Ruhul Amin
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„Mann“ is to “Donna” as「国王」is to « Reine » Adapting the Analogy Task for Multilingual and Contextual Embeddings (2023.starsem-1)
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| Challenge: | a lack of comparable multilingual benchmarks and a consensual evaluation protocol for contextual models remains an open question. |
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| Challenge: | Abstract Meaning Representation (AMR) is a semantic formalism that captures the core meaning of an utterance. |
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Seeking Clozure: Robust Hypernym extraction from BERT with Anchored Prompts (2023.starsem-1)
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| Challenge: | Existing methods for extracting hypernym knowledge from large language models are unclear whether they fail due to a lack of knowledge or shortcomings. |
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A Tale of Two Laws of Semantic Change: Predicting Synonym Changes with Distributional Semantic Models (2023.starsem-1)
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| Challenge: | Lexical Semantic Change is the study of how the meaning of words evolves through time. |
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