Summary of Improving the Robustness Of Knowledge-grounded Dialogue Via Contrastive Learning, by Jiaan Wang et al.
Improving the Robustness of Knowledge-Grounded Dialogue via Contrastive Learning
by Jiaan Wang, Jianfeng Qu, Kexin Wang, Zhixu Li, Wen Hua, Ximing Li, An Liu
First submitted to arxiv on: 9 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach to improve the robustness of knowledge-grounded dialogue (KGD) systems in real-world applications. The authors aim to tackle the challenges posed by real-world noises, such as misspellings, abbreviations, incomplete or outdated knowledge graphs (KGs), and noisy inputs. To achieve this, they design an entity-based contrastive learning framework that utilizes both positive and negative samples with semantic-irrelevant and semantic-relevant perturbations, respectively. This approach enables the KGD model to generate informative responses even in the presence of noise. Experimental results on three benchmark datasets demonstrate the effectiveness of this method, achieving state-of-the-art performance in automatic evaluation scores. Additionally, the authors show that their approach can produce better responses than comparison models in both noisy and few-shot settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us build chatbots that talk to us about things they really know! They’re called knowledge-grounded dialogue systems, and they need to be super smart and strong to handle real-world noises like mistakes and outdated facts. To make them better, the authors came up with a new way to teach these chatbots using something called contrastive learning. It’s like training them to recognize when someone is trying to trick them or distract them! They tested this method on some big datasets and it worked really well, making their chatbot talk more wisely than others. |
Keywords
* Artificial intelligence * Few shot
