Papers with negative training
Diversifying Neural Dialogue Generation via Negative Distillation (2022.naacl-main)
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| Challenge: | Existing approaches to generate generic responses are ignoring low-frequency but generic responses and bringing low- frequency but meaningless responses. |
| Approach: | They propose a negative training paradigm that reminds dialogue models not to generate high-frequency responses during training. |
| Outcome: | The proposed method outperforms previous methods in the generic response problem while minimizing low-frequency but meaningless responses. |
I already said that! Degenerating redundant questions in open-domain dialogue systems. (2023.acl-srw)
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| Challenge: | Neural text generation models have been successful in short open-domain conversations, but their performance degrades significantly in the long term. |
| Approach: | They propose a method to generate training data without crowdsourcing . they adapt negative training, decoding, and classification methods to mitigate redundancy problem . |
| Outcome: | The proposed method reduces the rate of redundant questions from 27.2% to 8.7% while improving the quality of the original model. |
Rank-Aware Negative Training for Semi-Supervised Text Classification (2023.tacl-1)
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| Challenge: | Semi-supervised text classification-based paradigms employ the spirit of self-training, but the accuracy of pseudo-labels can be a problem in real-world scenarios. |
| Approach: | They propose a Rank-aware Negative Training framework to address SSTC in noisy label learning . they rank unlabeled texts based on evidential support from the labeled texts. |
| Outcome: | The proposed framework overcomes state-of-the-art alternatives and achieves competitive performance in other scenarios. |
Negative Training for Neural Dialogue Response Generation (2020.acl-main)
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| Challenge: | Existing models have undesirable generation behaviors, such as generic (boring) responses. |
| Approach: | They propose a framework to minimize undesirable generation behaviors in deep learning models . they use generated samples to feed negative training signals to fine-tune the model . |
| Outcome: | The proposed framework reduces the hit rate of malicious responses or discourages frequent responses and improves response diversity. |
SENT: Sentence-level Distant Relation Extraction via Negative Training (2021.acl-long)
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| Challenge: | Existing methods for relation extraction use bag labels, which introduce noise, to train the model. |
| Approach: | They propose to use negative training to train a model using complementary labels to separate the noisy data from the training data. |
| Outcome: | The proposed method improves on previous methods on sentence-level evaluation and de-noise effect. |