Papers with auto-encoders
An Auto-Encoder Matching Model for Learning Utterance-Level Semantic Dependency in Dialogue Generation (D18-1)
Copied to clipboard
| Challenge: | Experimental results show that our model can generate semantically coherent responses compared to baseline models. |
| Approach: | They propose an Auto-Encoder Matching model to learn utterance-level semantic dependency . their model contains two auto-encoders and one mapping module . |
| Outcome: | Experimental results show that the proposed model can generate high coherence and fluency compared to baseline models. |
ADePT: Auto-encoder based Differentially Private Text Transformation (2021.eacl-main)
Copied to clipboard
| Challenge: | Differential privacy is an important privacy concern when building statistical models on data containing sensitive information. |
| Approach: | They propose a utility-preserving differentially private text transformation algorithm using auto-encoders that can be used to transform text to offer robustness against attacks and produce transformations with high semantic quality. |
| Outcome: | The proposed model performs better against membership inference attacks while offering lower to no degradation in the utility of the underlying transformation process compared to baselines. |
Rule Augmented Unsupervised Constituency Parsing (2021.findings-acl)
Copied to clipboard
| Challenge: | Recent studies have shown that unsupervised parsing methods do not learn meaningful semantics (not even simple grammar) |
| Approach: | They propose an approach that utilizes very generic linguistic knowledge of the language present in the form of syntactic grammar rules and is independent of the base system. |
| Outcome: | The proposed model is independent of the base system and takes advantage of syntactic grammar rules. |
In Neural Machine Translation, What Does Transfer Learning Transfer? (2020.acl-main)
Copied to clipboard
| Challenge: | a recent study found that word embeddings are not necessary for transfer learning. |
| Approach: | They perform several ablation studies that limit information transfer and measure the quality impact across three language pairs to gain a black-box understanding of transfer learning. |
| Outcome: | The proposed method can eliminate the need for a warm-up phase when training transformer models in high resource language pairs. |