Quantifying the Contextualization of Word Representations with Semantic Class Probing (2020.findings-emnlp)
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| Challenge: | Pretrained language models are effective in solving NLP tasks, but there are still questions about how and why they work so well. |
| Approach: | They use BERT to quantify contextualization by studying the extent of inference . they show that top layer representations support highly accurate inference of semantic classes . |
| Outcome: | The proposed model is highly accurate, but weak in the lower layers . it is more task-specific after finetuning while lower layers are more transferable . |
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| Challenge: | Existing word embeddings were static, requiring all senses of a polysemous word to share the same representation. |
| Approach: | They found that the contextualized representations of all words are not isotropic in any layer of the contextualizing model. |
| Outcome: | The results show that the representations of all words are not isotropic in any layer of the contextualizing model. |
Linguistic Knowledge and Transferability of Contextual Representations (N19-1)
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| Challenge: | Recent work has explored contextual word representations, which assign each word a vector that is a function of the entire input sequence. |
| Approach: | They compare pretrained word representations with 16 diverse probing tasks to examine their transferability. |
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Explaining Contextualization in Language Models using Visual Analytics (2021.acl-long)
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| Challenge: | Contextualized language models (LMs) have learned highly transferable and task-agnostic properties of language, even to a degree of imitating the classical NLP pipeline. |
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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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Cracking the Contextual Commonsense Code: Understanding Commonsense Reasoning Aptitude of Deep Contextual Representations (D19-60)
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| Challenge: | Pretrained deep contextual representations have advanced the state-of-the-art on various commonsense NLP tasks, but we lack a concrete understanding of their capabilities. |
| Approach: | They investigate BERT's ability to encode various commonsense features in its embedding space, but are still deficient in many areas. |
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Interpreting Pretrained Contextualized Representations via Reductions to Static Embeddings (2020.acl-main)
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| Challenge: | Contextualized representations have become the default for downstream NLP applications. |
| Approach: | They propose a method for converting from contextualized representations to static lookup-table embeddings and apply it to 5 popular pretrained models and 9 sets of pretrained weights. |
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Picking BERT’s Brain: Probing for Linguistic Dependencies in Contextualized Embeddings Using Representational Similarity Analysis (2020.coling-main)
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| Challenge: | Contextualized word embeddings can incorporate contextual information, whereas other embeddables cannot. |
| Approach: | They propose an approach to address this question using Representational Similarity Analysis (RSA) they investigate whether verb embeddings encode verb’s subject, pronoun embedds antecedent and full-sentence representations encode sentence’s head word . |
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Improved Word Sense Disambiguation Using Pre-Trained Contextualized Word Representations (D19-1)
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| 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. |
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Which *BERT? A Survey Organizing Contextualized Encoders (2020.emnlp-main)
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| Challenge: | a survey on language representation learning aims to highlight common themes . we focus on the areas of progress, compared to other fields, and discuss how each area is evaluated. |
| Approach: | They present a survey on language representation learning to highlight common themes . they compare contributions in contextualized text encoders to ideas from other fields . |
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