Spot the Odd Man Out: Exploring the Associative Power of Lexical Resources (D18-1)
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| Challenge: | Existing word embeddings assign only one vector to each word, resulting in word disambiguation on smaller scales. |
| Approach: | They propose a task which aims to test different properties of word representations. |
| Outcome: | The proposed task is intuitive enough to annotate on a large scale while teasing out properties of popular lexical resources. |
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| Challenge: | WordNets are lexical databases in which groups of synonyms are stored according to the semantic relationships between them. |
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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. |
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What Does This Word Mean? Explaining Contextualized Embeddings with Natural Language Definition (D19-1)
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| Challenge: | Contextualized word embeddings have boosted many NLP tasks compared with static word embeds. |
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Spying on Your Neighbors: Fine-grained Probing of Contextual Embeddings for Information about Surrounding Words (2020.acl-main)
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| Challenge: | a suite of probing tasks test contextual embeddings for encoding of information about surrounding words . authors: little is known about what information embeddables encode about the context words encode . a recent study shows that contextual embeds can be powerful for many tasks . |
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Contextual Embeddings: When Are They Worth It? (2020.acl-main)
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| Challenge: | In recent years, rich contextual embeddings have enabled rapid progress on benchmarks like GLUE, but require significant computational resources during pretraining and during downstream task training and inference. |
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High Quality ELMo Embeddings for Seven Less-Resourced Languages (2020.lrec-1)
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| Challenge: | Recent results show that deep neural networks using contextual embeddings outperform non-contextual embedders on a majority of text classification tasks. |
| Approach: | They propose to use contextual embeddings for seven languages to train new embeddables . they also show that existing embeddibles for listed languages shall be improved . |
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Analyzing the Surprising Variability in Word Embedding Stability Across Languages (2021.emnlp-main)
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| Challenge: | Word embeddings are powerful representations that form the foundation of many natural language processing architectures. |
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Searching for the X-Factor: Exploring Corpus Subjectivity for Word Embeddings (P18-1)
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| Challenge: | Existing word embedding methods for natural language processing are limited in their ability to produce dense word embeds. |
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Embeddings in Natural Language Processing (2020.coling-tutorials)
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| Challenge: | Embeddings have been a key topic of interest in NLP for the past decade . a quick warm-up introduction to NLP and why it is important to have a semantic comprehension of texts . |
| Approach: | This tutorial will provide a high-level synthesis of the main embedding techniques in NLP . it will start with word embedds and then move to other types of embeddable vectors . |
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Lexi: A tool for adaptive, personalized text simplification (C18-1)
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| Challenge: | Existing research on text simplification has aimed to develop generic solutions . instead, we need to develop customized simplification systems for individual users . |
| Approach: | They propose a framework for adaptive lexical simplification and introduce Lexi, a free open-source tool for personalized text simplification. |
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