| Challenge: | Existing approaches to relation induction are based on vector translations, but they are often inadequate for knowledge base completion. |
| Approach: | They propose to use Gaussian to explicitly model the variability of translations and Bayesian linear regression to encode the assumption that there is a linear relationship between the vector representations of related words. |
| Outcome: | The proposed models are based on translations but use Gaussian to model the variability of translations and encode soft constraints on the source and target words that may be chosen. |
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| Challenge: | Existing approaches to relation extraction use concatenating embeddings of head and tail entities . however, such representations capture the types of the entities involved, leading to false positives and confusion between relations involving entities of the same type. |
| Approach: | They propose a model which combines [MASK] embeddings with entity embedds to learn relation embeddations. |
| Outcome: | The proposed model outperforms the state-of-the-art on several benchmarks . it uses a self-supervised pre-training strategy which further improves the results. |
Distilling Relation Embeddings from Pretrained Language Models (2021.emnlp-main)
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| Challenge: | Pre-trained language models capture a surprisingly rich amount of lexical knowledge, but it is unclear to what extent relation embeddings can be used to encode relational knowledge. |
| Approach: | They found that word vector differences capture lexical relations . relationship embeddings can be used to encode relational knowledge . |
| Outcome: | The results are highly competitive on analogy (unsupervised) and relation classification (supervised) benchmarks, even without any task-specific fine-tuning. |
Revisiting the Context Window for Cross-lingual Word Embeddings (2020.acl-main)
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| Challenge: | Existing approaches to mapping-based cross-lingual word embeddings are based on the assumption that the source and target embeddable spaces are structurally similar. |
| Approach: | They propose to use different context windows to evaluate bilingual word embeddings in various languages, domains, and tasks. |
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Embedding Learning Through Multilingual Concept Induction (P18-1)
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| Challenge: | Existing methods for learning vector space representations of words are based on word-context information. |
| Approach: | They propose a method for estimating vector space representations of words by concept induction. |
| Outcome: | The proposed method performs better on crosslingual word similarity and sentiment analysis on a parallel corpus. |
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 . |
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A Deeper Look into Dependency-Based Word Embeddings (N18-4)
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| Challenge: | Word embeddings trained with dependency contexts excel at different tasks, and enhanced dependencies often improve performance. |
| Approach: | They propose to use dependency-based word embeddings to capture semantic similarity rather than relatedness. |
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Conditional Word Embedding and Hypothesis Testing via Bayes-by-Backprop (D18-1)
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| Challenge: | Whether word's meaning varies across contexts has become a major focus of research in recent years. |
| Approach: | They propose a word embedding model that incorporates document covariates to estimate conditional word embeds. |
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Global Relation Embedding for Relation Extraction (N18-1)
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| Challenge: | Existing methods to extract textual relations with distant supervision are limited by their reliance on supervised training data. |
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| Outcome: | The proposed method is more robust to training noise introduced by distant supervision and improves relation extraction models. |
Relational Word Embeddings (P19-1)
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| Challenge: | Existing approaches to learn word embeddings rely on external knowledge bases . however, they are limited by the amount of available relational knowledge . |
| Approach: | They propose to encode relational knowledge in a separate word embedding . this is complementary to a standard word embedded from co-occurrence statistics . |
| Outcome: | The proposed word embedding is complementary to a standard word embed. |
Evaluating bilingual word embeddings on the long tail (N18-2)
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| Challenge: | Bilingual word embeddings are useful for bilingual lexicon induction, but they focus on frequent words in general domains. |
| Approach: | They propose to evaluate bilingual word embeddings on rare words in different domains . they propose to use a multilingual dataset to build and combine BWEs based on a single word . |
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