Papers with word2vec models
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. |
Evaluating Word Embeddings in Extremely Under-Resourced Languages: A Case Study in Bribri (2022.coling-1)
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| Challenge: | a case study of word embeddings in an under-resourced context is presented . Embeddings are critical for many NLP tasks, but their evaluation in actual under-sourced settings needs further investigation. |
| Approach: | They adapt word embeddings to an under-resourced indigenous language in Bribri . they find that the best models find the appropriate semantic target 60% of the time . |
| Outcome: | The proposed models were adapted to an under-resourced English language . the best models found the appropriate semantic target 60% of the time . |
Using Convolution Neural Network with BERT for Stance Detection in Vietnamese (2022.lrec-1)
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| Challenge: | Stance detection is a task of automatically eliciting stance information towards a specific claim made by a primary author. |
| Approach: | They propose an architecture using transformers to detect stances in Vietnamese claims . they exploit BERT to extract contextual word embeddings instead of traditional word2vec models . |
| Outcome: | The proposed model outperforms the previous methods on a public dataset. |