| Challenge: | Word embedding is considered an essential factor in improving the performance of various Natural Language Processing (NLP) models. |
| Approach: | They propose a Hangeul word embedding model that infers original word embeds from typos while maintaining high performance. |
| Outcome: | The proposed model performs well against typos while maintaining high performance. |
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Robust Backed-off Estimation of Out-of-Vocabulary Embeddings (2020.findings-emnlp)
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| Challenge: | Existing approaches to solving out-of-vocabulary (OOV) words use subwords to represent oov words with a bag of subword. |
| Approach: | They propose a method to estimate oov word embeddings by referring to pre-trained word embeds for known words with similar surfaces to target ov words. |
| Outcome: | The proposed method improves word similarity tasks and biomedical tasks even with weak baselines. |
Few-Shot Representation Learning for Out-Of-Vocabulary Words (P19-1)
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| Challenge: | Existing methods for learning word embedding assume there are enough occurrences for each word in the corpus to accurately estimate the representation of words. |
| Approach: | They propose to fit a representation function to predict an oracle embedding vector based on limited contexts. |
| Outcome: | The proposed model outperforms existing methods in constructing an accurate embedding for OOV words and improves downstream tasks when the embeddable is utilized. |
Improving Word Embeddings through Iterative Refinement of Word- and Character-level Models (2020.coling-main)
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| Challenge: | Embedding of rare and out-of-vocabulary words is an important open NLP problem . standard embedding models are not useful for recommending jobs to users with rare or unseen words . |
| Approach: | They propose to train a character-level neural network to reproduce word embeddings . they then use the model to assign vectors to any input string, including rare words . |
| Outcome: | The proposed method outperforms existing methods on word similarity data sets and can be applied to job title normalization in the e-recruitment domain. |
Imputing Out-of-Vocabulary Embeddings with LOVE Makes LanguageModels Robust with Little Cost (2022.acl-long)
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| Challenge: | State-of-the-art NLP systems are brittle when faced with Out-ofVocabulary words . we present a framework that extends word embeddings and makes them robust to OOV . |
| Approach: | They propose a framework that extends existing word embeddings and makes them robust to OOV. |
| Outcome: | The proposed model performs better on original datasets and corrupted variants than previous competitors. |
COIN – an Inexpensive and Strong Baseline for Predicting Out of Vocabulary Word Embeddings (2022.coling-1)
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| Challenge: | Word embedding models only include terms that occur a sufficient number of times in training corpora. |
| Approach: | They propose a method for predicting word embeddings for out of vocabulary terms using word2vec. |
| Outcome: | The proposed method surpasses several methods on benchmark tasks and is inexpensive to compute. |
Representation Learning for Unseen Words by Bridging Subwords to Semantic Networks (2020.lrec-1)
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| Challenge: | Pre-trained word embeddings only include words that appeared in corpora where pre-tried embedds are learned. |
| Approach: | They propose a method to represent out-of-vocabulary words using subword information and knowledge. |
| Outcome: | The proposed method improves performance over baselines that only use subwords or knowledge to represent OOV words. |
Better Word Embeddings by Disentangling Contextual n-Gram Information (N19-1)
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| Challenge: | Pre-trained word vectors are ubiquitous in Natural Language Processing applications. |
| Approach: | They show that word embeddings with bigram and trigram embedds improve unigram embeds . they claim this removes contextual information from unigrammes, resulting in better unigraph embedders . |
| Outcome: | The proposed model outperforms competing models on a wide variety of tasks. |
How to represent a word and predict it, too: Improving tied architectures for language modelling (D18-1)
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| Challenge: | Recent state-of-the-art models use word embeddings as input and output mappings instead of tied models. |
| Approach: | They propose to decouple hidden state from word embedding prediction . they extend their proposed modification to word2vec models . |
| Outcome: | The proposed architectures achieve comparable or better results compared to previous models without tying . the proposed architecture reduces parameters, enabling more compact models and faster learning. |
Graph-based Relation Mining for Context-free Out-of-vocabulary Word Embedding Learning (2023.acl-long)
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| Challenge: | Existing word embedding methods fail to model complex word formation well. |
| Approach: | They propose a graph-based relation mining method for OOV word embedding learning that can infer high-quality embeddables for OV words through passing and aggregating semantic attributes and relational information in the WRG. |
| Outcome: | The proposed method outperforms state-of-the-art models on both intrinsic and downstream tasks when faced with OOV words. |
Enhancing Out-of-Vocabulary Estimation with Subword Attention (2023.findings-acl)
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| Challenge: | Existing methods to learn OOV word representations use advanced architectures like attention on the context of the word, but they tend to use simple structures like ngram addition or character based convolutional neural networks (CNN) |
| Approach: | They propose a transformer-based OOV estimation model that uses attention mechanisms on both the context and the subwords to learn OOV representations. |
| Outcome: | The proposed model outperforms current state-of-the-art models on OOV representations based on attention mechanisms on the context and subwords . |