自然语言处理顶级会议

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最近的研究表明,许多NLP系统对输入的微小扰动非常敏感和脆弱,并且不能很好地在不同的数据集上进行泛化。这种鲁棒性的缺乏阻碍了NLP系统在真实应用中的使用。本教程旨在让人们意识到有关NLP鲁棒性的实际问题。它的目标是对构建可靠的NLP系统感兴趣的NLP研究人员和实践者。特别地,我们将回顾最近关于分析NLP系统在面对对抗输入和分布转移数据时的弱点的研究。我们将为观众提供一个全面的视角

如何使用对抗性的例子来检查NLP模型的弱点并促进调试; 如何增强现有NLP模型的鲁棒性和对抗输入的防御; 对鲁棒性的考虑如何影响我们日常生活中使用的真实世界的NLP应用。 我们将通过概述这一领域未来的研究方向来结束本教程。

https://robustnlp-tutorial.github.io

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This paper presents the participation of NetEase Game AI Lab team for the ClariQ challenge at Search-oriented Conversational AI (SCAI) EMNLP workshop in 2020. The challenge asks for a complete conversational information retrieval system that can understanding and generating clarification questions. We propose a clarifying question selection system which consists of response understanding, candidate question recalling and clarifying question ranking. We fine-tune a RoBERTa model to understand user's responses and use an enhanced BM25 model to recall the candidate questions. In clarifying question ranking stage, we reconstruct the training dataset and propose two models based on ELECTRA. Finally we ensemble the models by summing up their output probabilities and choose the question with the highest probability as the clarification question. Experiments show that our ensemble ranking model outperforms in the document relevance task and achieves the best recall@[20,30] metrics in question relevance task. And in multi-turn conversation evaluation in stage2, our system achieve the top score of all document relevance metrics.

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