| Challenge: | Stochastic Gradient Descent with negative sampling is the most prevalent approach to learn word representations. |
| Approach: | They propose a method that uses batch gradient learning to generate word representations from all training samples. |
| Outcome: | The proposed method outperforms sampling-based methods on several benchmark tasks. |
Similar Papers
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. |
Debiased Contrastive Learning of Unsupervised Sentence Representations (2022.acl-long)
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| Challenge: | Recent studies have shown that contrastive learning improves pre-trained language models to derive high-quality sentence representations. |
| Approach: | They propose a framework to punish false negatives and generate noise-based negatives to guarantee the uniformity of the representation space. |
| Outcome: | The proposed framework improves pre-trained language models while pushing apart irrelevant negatives to guarantee the uniformity of the representation space. |
GNEG: Graph-Based Negative Sampling for word2vec (P18-2)
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| Challenge: | Generally speaking, negative sampling is the best choice for distributed word representation learning. |
| Approach: | They hypothesize that taking into account global, corpus-level information and generating a different noise distribution for each target word better satisfies the requirements of negative examples for each training word. |
| Outcome: | The proposed approach boosts the word analogy task by about 5% and improves the performance on word similarity tasks by about 11% compared to the baseline. |
Negative Sampling Techniques in Dense Retrieval: A Survey (2026.findings-eacl)
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| Challenge: | Information Retrieval (IR) is fundamental to many modern NLP applications. |
| Approach: | They propose a taxonomy that categorizes negative sampling techniques in dense IR . they analyze them with respect to trade-offs between effectiveness, computational cost, implementation difficulty . |
| Outcome: | The proposed taxonomy categorizes techniques using random, static/dynamically mined, and synthetic datasets. |
Clustering-Aware Negative Sampling for Unsupervised Sentence Representation (2023.findings-acl)
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| Challenge: | Using clustering-aware learning, in-batch negatives are often ignored in sentence representation learning. |
| Approach: | They propose a method that integrates cluster information into contrastive learning for unsupervised sentence representation learning. |
| Outcome: | The proposed method compares favorably with baselines on semantic textual similarity tasks. |
End-to-End Bias Mitigation by Modelling Biases in Corpora (2020.acl-main)
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| Challenge: | Recent studies have shown that strong natural language understanding models are prone to relying on unwanted dataset biases without learning the underlying task. |
| Approach: | They propose two learning strategies to train neural models that are more robust to dataset biases and transfer better to out-of-domain datasets. |
| Outcome: | The proposed methods improve robustness in all settings and transfer better to out-of-domain datasets. |
Accelerating Asynchronous Stochastic Gradient Descent for Neural Machine Translation (D18-1)
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| Challenge: | In order to achieve faster training we increase the mini-batch size and scale the learning rate accordingly. |
| Approach: | They propose a technique that delays gradient updates by increasing the mini-batch size to improve the model's convergence. |
| Outcome: | The proposed technique can train a shallow machine translation system 27% faster than an optimized baseline with negligible penalty in BLEU. |
Unsupervised Cross-Lingual Representation Learning (P19-4)
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| Challenge: | a comprehensive survey of cutting-edge weakly-supervised and unsupervised cross-lingual word representations is presented . |
| Approach: | This tutorial provides a comprehensive survey of recent work on weakly-supervised and unsupervised cross-lingual word representations. |
| Outcome: | This tutorial provides a comprehensive survey of cutting-edge weakly-supervised and unsupervised word representations. |
A Natural Bias for Language Generation Models (2023.acl-short)
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| Challenge: | a standard probabilistic model for language generation has likely not yet learnt many semantic or syntactic rules of natural language, making it difficult to estimate the probability distribution over next tokens. |
| Approach: | They propose to initialise bias terms in a model's final linear layer with the log-unigram distribution and use it to output the unigram frequency statistics as prior knowledge. |
| Outcome: | The proposed method improves learning efficiency and improves overall performance. |
Learning Word Vectors for 157 Languages (L18-1)
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| Challenge: | Distributed word representations, or word vectors, have been used in natural language processing for many tasks. |
| Approach: | They propose to use the encyclopedia Wikipedia and the common crawl corpus to train distributed word representations on large corpora and use them in downstream tasks. |
| Outcome: | The proposed model performs very well on 10 languages for which evaluation dataset exists. |