Papers with adversarial networks
Multi-Stage Framework with Refinement Based Point Set Registration for Unsupervised Bi-Lingual Word Alignment (2022.coling-1)
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| Challenge: | Existing unsupervised approaches to cross-lingual word embeddings suffer from instability and convergence issues. |
| Approach: | They propose a multi-stage framework for unsupervised mapping of bi-lingual word embeddings onto a shared vector space by combining adversarial initialization, refinement procedure and point set registration. |
| Outcome: | The proposed framework shows robustness against variable adversarial performance on diverse languages. |
Multinomial Adversarial Networks for Multi-Domain Text Classification (N18-1)
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| Challenge: | Existing methods for text classification are domain-dependent, but there is no annotated data for some domains. |
| Approach: | They propose a multinomial adversarial network to tackle multi-domain text classification . they show that MANs significantly outperform prior art on the MDTC task . |
| Outcome: | The proposed model outperforms the prior art on the multi-domain text classification task. |
Multi-Source Cross-Lingual Model Transfer: Learning What to Share (P19-1)
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| Challenge: | Cross-lingual transfer learning (CLTL) is a viable method for building NLP models for a low-resource target language . however, many languages lack the labeled training data necessary for training deep neural nets for varying NLP tasks. |
| Approach: | They propose a cross-lingual transfer learning method that leverages annotated data from other languages to build NLP models for a target language. |
| Outcome: | The proposed model achieves significant performance gains over prior art over multiple text classification and sequence tagging tasks including a large-scale industry dataset. |
Representation Alignment and Adversarial Networks for Cross-lingual Dependency Parsing (2024.findings-emnlp)
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| Challenge: | Pre-trained language models have improved dependency parsing accuracy in resource-rich languages . however, the accuracy drops sharply when the model is transferred to low-resource language . |
| Approach: | They propose a representation alignment and adversarial model to filter out useful knowledge from rich-resource language and ignore useless ones. |
| Outcome: | The proposed model outperforms baseline models on the benchmark datasets by 1.37 LAS and 1.34 UAS. |