Summary of A Static and Dynamic Attention Framework For Multi Turn Dialogue Generation, by Wei-nan Zhang et al.
A Static and Dynamic Attention Framework for Multi Turn Dialogue Generation
by Wei-Nan Zhang, Yiming Cui, Kaiyan Zhang, Yifa Wang, Qingfu Zhu, Lingzhi Li, Ting Liu
First submitted to arxiv on: 28 Oct 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 Recently, research on open-domain dialogue systems has garnered significant interest from both academia and industry. The goal is to develop systems that can mimic human conversations. Previous studies have made substantial progress in single-turn conversation generation, but understanding multiple single-turn conversations does not necessarily translate to comprehending multi-turn dialogues. This is because human dialogue exhibits coherent and context-dependent properties. In open-domain multi-turn dialogue generation, it’s crucial to model the contextual semantics of the dialogue history rather than solely relying on the last utterance. The hierarchical recurrent encoder-decoder framework has been shown to be effective in previous research. However, using RNN-based models to hierarchically encode utterances for dialogue history representation still faces the issue of vanishing gradients. To address this, we propose a static and dynamic attention-based approach to model dialogue history and generate open-domain multi-turn dialogue responses. Experimental results on Ubuntu and Opensubtitles datasets demonstrate the effectiveness of our proposed approach in various settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine having a conversation with a computer that feels like talking to a friend. Researchers have been working on creating systems that can do just that. They want these systems to be able to understand and respond to multiple turns of conversation, not just one single turn. To make this happen, they need to figure out how to model the context of the conversation. In other words, they need to understand what happened before and how it relates to what’s happening now. Previous attempts have been successful, but there are still some issues to work through. To solve these problems, we propose a new approach that uses attention to help the computer focus on the most important parts of the conversation. We tested our approach on two big datasets and saw great results. |
Keywords
» Artificial intelligence » Attention » Encoder decoder » Rnn » Semantics
