| Challenge: | Pretrained transformer-based encoders can be used to acquire rich text representations but need additional task supervision to be useful for downstream tasks. |
| Approach: | They propose a transformer to each MetaEval task and a neural network with a weighted encoder to perform the embeddings. |
| Outcome: | The proposed model outperforms baselines on GLUE tasks and can be used as a benchmark for future transfer learning research. |
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Learning Efficient Task-Specific Meta-Embeddings with Word Prisms (2020.coling-main)
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| Challenge: | Word embeddings possess different lexical properties depending on the notion of context defined at training time. |
| Approach: | They introduce a meta-embedding method that learns to combine source embeddings according to the task at hand. |
| Outcome: | The proposed method improves performance on six extrinsic evaluations over other methods. |
Towards Unified Task Embeddings Across Multiple Models: Bridging the Gap for Prompt-Based Large Language Models and Beyond (2024.findings-acl)
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| Challenge: | Existing task embedding methods rely on fine-tuned, task-specific language models, which hinders their adaptability to prompt-guided Large Language Models (LLMs). |
| Approach: | They propose a framework for unified task embedding that harmonizes task embeds from various models within a single vector space. |
| Outcome: | The proposed framework harmonizes task embeddings from various models within a single vector space. |
Is Language Modeling Enough? Evaluating Effective Embedding Combinations (2020.lrec-1)
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Rudolf Schneider, Tom Oberhauser, Paul Grundmann, Felix Alexander Gers, Alexander Loeser, Steffen Staab
| Challenge: | specialized embeddings are not available for tasks like entity linking or paragraph classification. |
| Approach: | They evaluate whether universal embeddings can be complemented by specialized embeddables. |
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General Purpose Text Embeddings from Pre-trained Language Models for Scalable Inference (2020.findings-emnlp)
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| Challenge: | Large pre-trained language models are currently used for many NLP tasks . however, inference for these models requires significant computational resources . |
| Approach: | They propose to use a shared text encoder to amortize the computational cost of inference over multiple tasks. |
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Dynamic Meta-Embeddings for Improved Sentence Representations (D18-1)
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| Challenge: | A sprawling literature has emerged about what word embeddings are most useful for which tasks . word embed-ding is a technique that can be used to learn word-level meaning representations for a variety of tasks. |
| Approach: | They propose a method for supervised learning of embedding ensembles that leads to state-of-the-art performance on a variety of tasks. |
| Outcome: | The proposed method leads to state-of-the-art performance on a variety of tasks. |
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 . |
| Outcome: | This tutorial will provide a high-level synthesis of the main embedding techniques in NLP . it will start with word embedds and move to other types of embeddable representations . |
What Do Position Embeddings Learn? An Empirical Study of Pre-Trained Language Model Positional Encoding (2020.emnlp-main)
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| Challenge: | Existing work on pre-trained Transformers has focused on learning the meaning of positions . Embedding the position information in the self-attention mechanism is also an indispensable factor in NLP . |
| Approach: | They propose to use feature-level analysis to examine pre-trained Transformers' position embeddings . they also use empirical experiments to determine the appropriate positional encoding function . |
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What’s in Your Embedding, And How It Predicts Task Performance (C18-1)
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| Challenge: | Attempts to find a single technique for general-purpose intrinsic evaluation of word embeddings have so far not been successful. |
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Task-oriented Domain-specific Meta-Embedding for Text Classification (2020.emnlp-main)
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| Challenge: | Existing methods neglect domain-specific knowledge and use the same word embedding for each word in all domain-specified datasets. |
| Approach: | They propose a method to incorporate domain-specific and task-oriented information into meta-embeddings by combining pre-trained word embeddings. |
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Meta-Task Prompting Elicits Embeddings from Large Language Models (2024.acl-long)
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| Challenge: | Existing methods for large language modeling are based on task-related instructions or prompts. |
| Approach: | They propose a method for generating high-quality sentence embeddings from Large Language Models (LLMs) using meta-task prompts. |
| Outcome: | The proposed method produces high-quality sentences without fine-tuning . it excels on STS benchmarks and in downstream tasks, surpassing models with similar prompts . |