| Challenge: | Recent work has attempted to enhance vector space representations using information from structured semantic resources. |
| Approach: | They propose a root-mean-square error evaluation metric to evaluate the utility of different lexical resources for retrofitting. |
| Outcome: | The proposed method improves word similarity performance by using root-mean-square error (RMSE) and root-macro-error (RMME) metric. |
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Explicit Retrofitting of Distributional Word Vectors (P18-1)
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| Challenge: | Existing models for word vector specialization focus on word co-occurrences from large text corpora, resulting in a tendency to fuse semantic similarity with other types of semantic relatedness. |
| Approach: | They propose to transform external lexico-semantic relations into training examples and learn an explicit retrofitting model. |
| Outcome: | The proposed model can specialize vector spaces of new languages and translate them to other languages. |
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. |
A Retrofitting Model for Incorporating Semantic Relations into Word Embeddings (2020.coling-main)
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| Challenge: | Existing word embedding models mix semantic similarity with other types of relatedness. |
| Approach: | They propose a model that leverages relational knowledge available in a knowledge resource to improve word embeddings. |
| Outcome: | The proposed model improves word embeddings on synonymy, antonymy and hypernymy relations in WordNet and significantly improves lexical entailment detection task. |
On the Correlation of Word Embedding Evaluation Metrics (2020.lrec-1)
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| Challenge: | Word embeddings are geometrical representations of word paradigmatics and syntagmatics. |
| Approach: | They propose to investigate evaluation metrics on various datasets to find correlations . they propose a fast solution to select the best word embeddings among many others . |
| Outcome: | The proposed method could be used to select the best word embeddings among many others. |
Using Context-to-Vector with Graph Retrofitting to Improve Word Embeddings (2022.acl-long)
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| Challenge: | Contextualized embeddings are expensive and resource-demanding, hence environmentally unfriendly. |
| Approach: | They propose a method to convert contextualized embeddings from pre-trained models into static embeddables using synonym knowledge and weighted vector distribution. |
| Outcome: | The proposed method outperforms baseline embeddings by a large margin through extrinsic and intrinsic tasks. |
CoSimLex: A Resource for Evaluating Graded Word Similarity in Context (2020.lrec-1)
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Carlos Santos Armendariz, Matthew Purver, Matej Ulčar, Senja Pollak, Nikola Ljubešić, Mark Granroth-Wilding
| Challenge: | Existing methods to evaluate word embeddings ignore context and treat words in isolation. |
| Approach: | They propose to build a new word embeddings-based dataset that provides context-dependent similarity measures. |
| Outcome: | The proposed dataset provides context-dependent similarity measures and covers a well-resourced language (English) but a number of less-resource languages. |
Butterfly Effects in Frame Semantic Parsing: impact of data processing on model ranking (C18-1)
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| Challenge: | a common contribution to computational linguistics research is a new model for a specific task. |
| Approach: | They propose an open-source standardized processing pipeline for frame semantic parsing . they propose a standard evaluation resource that can be shared and reused for robust comparison . |
| Outcome: | The proposed model can be shared and reused for robust model comparison. |
From Text to Lexicon: Bridging the Gap between Word Embeddings and Lexical Resources (C18-1)
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| Challenge: | Distributional word representations are omnipresent in modern NLP. |
| Approach: | They propose to combine lemmatization and part of speech (POS) typing to improve word embedding performance. |
| Outcome: | The proposed methods improve word embedding performance on verbs and verbs. |
Advances in Pre-Training Distributed Word Representations (L18-1)
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| Challenge: | Pre-trained word representations are a building block of many Natural Language Processing and Machine Learning applications. |
| Approach: | They propose to combine known tricks and a set of publicly available pre-trained word vector representations to train high-quality representations. |
| Outcome: | The proposed models outperform the current state of the art on a number of tasks while maintaining a high training speed to scale to massive amount of data. |
Frustratingly Easy Meta-Embedding – Computing Meta-Embeddings by Averaging Source Word Embeddings (N18-2)
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| Challenge: | Existing methods for producing word embeddings have shown to produce accurate meta-embeddings from pre-trained source embeddables. |
| Approach: | They propose to use arithmetic mean of two distinct word embedding sets to produce an accurate meta-embedding. |
| Outcome: | The proposed method produces meta-embeddings comparable or better than more complex methods. |