| 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. |
Similar Papers
Synthesizing Adversarial Negative Responses for Robust Response Ranking and Evaluation (2021.findings-acl)
Copied to clipboard
| Challenge: | Open-domain neural dialogue models have achieved high performance in response ranking and evaluation tasks. |
| Approach: | They propose methods for automatically creating adversarial negative training data . they use mask-and-fill and keyword-guided approaches to generate negative examples . |
| Outcome: | The proposed approaches outperform baseline models in providing informative negative examples for training dialogue systems. |
Diversifying Neural Dialogue Generation via Negative Distillation (2022.naacl-main)
Copied to clipboard
| 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. |
Counterfactual Off-Policy Training for Neural Dialogue Generation (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing models for open-domain dialogue generation suffer from data insufficiency . a potential response inferred in hindsight is called a counterfactual reasoning . |
| Approach: | They propose to explore potential responses by counterfactual reasoning . given an observed response, the model automatically infers the outcome of an alternative policy that could have been taken . |
| Outcome: | The proposed model outperforms the HRED model and conventional learning frameworks on the DailyDialog dataset. |
Pneg: Prompt-based Negative Response Generation for Dialogue Response Selection Task (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for synthesizing adversarial negative responses are limited by their scalability and cost. |
| Approach: | They propose a method for generating adversarial negative responses using a large-scale language model. |
| Outcome: | The proposed method outperforms other methods on dialogue selection tasks. |
Mitigating Gender Bias for Neural Dialogue Generation with Adversarial Learning (2020.emnlp-main)
Copied to clipboard
| Challenge: | Recent research shows that dialogue systems trained on human conversation data are biased and can produce responses that reflect people’s gender prejudice. |
| Approach: | They propose a novel adversarial learning framework Debiased-Chat to train dialogue models free from gender bias while keeping their performance. |
| Outcome: | The proposed framework significantly reduces gender bias in dialogue models while maintaining the response quality. |
Group-wise Contrastive Learning for Neural Dialogue Generation (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches to training dialogue models have low diversity in open-domain contexts . prior art suggests that naive MLE objective is not effective enough . |
| Approach: | They propose to incorporate contrastive learning into dialogue generation by using a pretrained baseline model as a reference. |
| Outcome: | The proposed framework is suited for training a wide range of dialogue generation models with favorable performance over baseline training approaches. |
Learning from Perturbations: Diverse and Informative Dialogue Generation with Inverse Adversarial Training (2021.acl-long)
Copied to clipboard
| Challenge: | Inverse Adversarial Training (IAT) algorithm for training neural dialogue systems to avoid generic responses and model dialogue history better. |
| Approach: | They propose an algorithm that encourages the model to be sensitive to perturbations in dialogue history and learn from perturbations. |
| Outcome: | The proposed approach can model dialogue history better and generate more diverse responses on two benchmark datasets. |
The CRINGE Loss: Learning what language not to model (2023.acl-long)
Copied to clipboard
| Challenge: | Standard language model training uses gold human documents or human-human interaction data and treats all training data as positive examples. |
| Approach: | They propose a procedure to train with negative examples using the "CRINGE" loss technique and use it to train models with such data. |
| Outcome: | The proposed procedure outperforms multiple strong baselines and is simple to train and implement. |
Less is More: Mitigate Spurious Correlations for Open-Domain Dialogue Response Generation Models by Causal Discovery (2023.tacl-1)
Copied to clipboard
| Challenge: | Existing models suffer from spurious correlations and generate irrelevant and generic responses. |
| Approach: | They propose a model-agnostic method for training and inference using a conditional independence classifier that overcomes data sparsity. |
| Outcome: | The proposed method outperforms the baseline models in relevance, informativeness, and fluency. |
Robust Task-Oriented Dialogue Generation with Contrastive Pre-training and Adversarial Filtering (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Task-oriented dialogue models can learn non-transferable generalizations by using shortcuts in the data. |
| Approach: | They propose a contrastive learning framework to encourage models to ignore cues and focus on generalisable patterns. |
| Outcome: | The proposed framework performs exceptionally well on task-oriented dialogue datasets. |