Papers with word similarity
Rare Tokens Degenerate All Tokens: Improving Neural Text Generation via Adaptive Gradient Gating for Rare Token Embeddings (2022.acl-long)
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| Challenge: | Recent studies have determined that the learned token embeddings of large-scale neural language models are degenerated to be anisotropic with a narrow-cone shape. |
| Approach: | They propose a method to degenerate the learning gradient for rare token embeddings by gating the specific part of the gradient for all tokens during training stage. |
| Outcome: | The proposed method improves the performance of the models but lacks the training dynamics needed to solve the representation degeneration problem. |
Enhancing Word Embeddings with Knowledge Extracted from Lexical Resources (2020.acl-srw)
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| Challenge: | In this paper, we present an effective method for semantic specialization of word vector representations. |
| Approach: | They propose a method for semantic specialization of word vector representations using BabelNet. |
| Outcome: | The proposed method improves on word similarity and dialog state tracking tasks. |
Can Network Embedding of Distributional Thesaurus Be Combined with Word Vectors for Better Representation? (N18-1)
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| Challenge: | Distributed representations of words learned from text have proved to be successful in various natural language processing tasks. |
| Approach: | They propose to embed a distributional thesaurus network into dense word vectors and compare them to state-of-the-art word representations. |
| Outcome: | The proposed representations improve performance against state-of-the-art word representations even without handcrafted lexical resources. |
Additive Compositionality of Word Vectors (D19-55)
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| Challenge: | Existing research on justifying additive compositionality of word embedding models requires a rather strong assumption of uniform word distribution. |
| Approach: | They propose to relax the assumption of uniform word distribution and propose more realistic conditions for proving additive compositionality. |
| Outcome: | The proposed model improves on word similarity and noisy sentence similarity. |
Querying Word Embeddings for Similarity and Relatedness (N18-1)
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| Challenge: | Word2Vec embeddings have become popular representations of word meaning . similarity between two words is often assumed to be a direction-less measure, whereas relatedness is inherently directional. |
| Approach: | They propose to use word embeddings to predict asymmetric association between words from a dataset of production norms to generate thematically related words. |
| Outcome: | The proposed model predicts asymmetric association between words from a recently published dataset of production norms. |
Incorporating Latent Meanings of Morphological Compositions to Enhance Word Embeddings (P18-1)
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| Challenge: | Existing word embedding methods learn semantic information at word level while neglecting meaningful inner structures of words like morphemes. |
| Approach: | They propose to use latent meanings of morphological compositions of words to train word embeddings. |
| Outcome: | The proposed models outperform baseline models on word similarity, syntactic analogy and text classification tasks. |
Constructing High Quality Sense-specific Corpus and Word Embedding via Unsupervised Elimination of Pseudo Multi-sense (L18-1)
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| Challenge: | Existing word embedding frameworks distinguish different senses of words by their contexts. |
| Approach: | They propose a framework for unsupervised corpus sense tagging which trains multi-sense word embeddings on a given corpus. |
| Outcome: | The proposed framework detects pseudo multi-senses without extra language resources without additional language resources. |
Retrofitting Word Representations for Unsupervised Sense Aware Word Similarities (L18-1)
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| Challenge: | Standard word embeddings lack the ability to distinguish senses of a word by projecting them to exactly one vector. |
| Approach: | They propose to retrofit standard word embeddings to produce sense-aware embeddable vectors using external resources as sense inventories. |
| Outcome: | The proposed method improves word similarity and relatedness scores on multiple word embeddings and established word similarities, sometimes up to an impressive margin of +0.15 Spearman correlation score. |
Subword-level Word Vector Representations for Korean (P18-1)
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| Challenge: | Existing research on word vectors for English focuses on decomposing words into subword units and using subwords to improve performance. |
| Approach: | They propose to decompose Korean words into the jamo-level, beyond the character-level . they develop Korean test sets for word similarity and analogy and make them publicly available . |
| Outcome: | The proposed method outperforms word2vec and character-level skip-grams on similarity and analogy tasks and contributes positively toward downstream NLP tasks such as sentiment analysis. |
Learning Numeral Embedding (2020.findings-emnlp)
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| Challenge: | Existing word embedding methods do not learn numeral embedds well because numerals are limited in number and their appearances in training corpora are highly scarce. |
| Approach: | They propose two numeral embedding methods that can handle the out-of-vocabulary problem for numerals. |
| Outcome: | The proposed methods can handle the out-of-vocabulary problem for numerals. |
Word Rotator’s Distance (2020.emnlp-main)
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| Challenge: | Existing approaches to measure textual similarity are inconsistent with the word alignment and are empirically inferior to the simple cosine similarity between general-purpose sentence vectors. |
| Approach: | They propose to decouple word vectors into their norm and direction and then grow the norm and directions of word vector. |
| Outcome: | The proposed methods outperform alignment-based approaches on several benchmarks and strong baselines on the semantic textual similarity task. |
Embedding Words in Non-Vector Space with Unsupervised Graph Learning (2020.emnlp-main)
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| Challenge: | GraphGlove is an unsupervised graph word representations that are learned end-to-end. |
| Approach: | They propose a method to learn weighted graph word representations end-to-end using a weighteable weighte . they adopt a hierarchical graph representation method and modify the GloVe training algorithm to learn graph representations. |
| Outcome: | The proposed method outperforms vector-based methods on word similarity and analogy tasks. |