| Challenge: | Word embeddings are used to encode semantic information, but their quality is not consistent across the vocabulary due to the long-tail distribution of word frequency. |
| Approach: | They propose a reliability-aware name tagging model that uses word frequency to indicate word quality . they propose to use word frequency-based reliability signals to dynamically select and compose features . |
| Outcome: | The proposed model outperforms the baseline model on OntoNotes 5.0 and up to 5% gain on cross-genre data sets. |
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| Challenge: | Recent work on word embeddings and pre-trained language models has shown the large impact of language representations on natural language processing (NLP) models across tasks and domains. |
| Approach: | They propose feature-based adversarial meta-embeddings with an attention function that is guided by word-specific properties, such as shape and frequency, to handle subword-based embeddings. |
| Outcome: | The proposed model improves performance in downstream tasks even with word embeddings from transformers. |
An Empirical Study of the Downstream Reliability of Pre-Trained Word Embeddings (2020.coling-main)
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| Challenge: | Pre-trained word embeddings have been shown to improve the performance of neural networks across a wide variety of tasks. |
| Approach: | They propose two new metrics to understand the downstream reliability of word embeddings. |
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Pooled Contextualized Embeddings for Named Entity Recognition (N19-1)
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| Challenge: | Contextual string embeddings are a recent type of word embeddable that are useful for sequence labeling tasks. |
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Dynamic Contextualized Word Embeddings (2021.acl-long)
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| Challenge: | Static word embeddings that represent words by a single vector cannot capture word meaning in different linguistic and extralinguistic contexts. |
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Exploring the Value of Personalized Word Embeddings (2020.coling-main)
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| Challenge: | a subset of words belonging to specific psycholinguistic categories vary more in their representations across users . combining generic and personalized word embeddings yields the best performance . |
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Are Word Embeddings Really a Bad Fit for the Estimation of Thematic Fit? (2020.lrec-1)
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| Challenge: | In recent years, vectors derived from neural network training have replaced count-based distributional semantic models as a de facto standard for word representation in NLP. |
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Attentive Mimicking: Better Word Embeddings by Attending to Informative Contexts (N19-1)
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| Challenge: | Mimicking has been proposed as a solution to learning high-quality embeddings for rare words because of sparse context information. |
| Approach: | They propose a method to reproduce embeddings of frequent words from their surface form and then use it to compute embedds for rare words. |
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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. |
| Approach: | They explore word embedding stability in a wide range of languages to gain insight into their stability. |
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An Investigation of the Interactions Between Pre-Trained Word Embeddings, Character Models and POS Tags in Dependency Parsing (D18-1)
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| Challenge: | Existing studies have shown that character models are less important in the presence of word embeddings, but combining them quickly leads to diminishing returns. |
| Approach: | They propose to combine pre-trained word embeddings, character models and POS tags to improve parsing quality by categorising words by frequency, POS tag and language. |
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Improving Embedding-based Large-scale Retrieval via Label Enhancement (2021.findings-emnlp)
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| Challenge: | Existing methods for large-scale retrieval are trained with 0-1 hard labels that indicate whether a query is relevant to a document, ignoring rich information of the relevance degree. |
| Approach: | They propose to introduce label enhancement for the first time to characterize query-document relevance degree by embedding label distribution into contextual embeddables. |
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