Slot filling and intent prediction are basic tasks in capturing semantic frame of human utterances.Slots and intent have strong correlation for semantic frame parsing.For each utterance,a specific intent type is gener...Slot filling and intent prediction are basic tasks in capturing semantic frame of human utterances.Slots and intent have strong correlation for semantic frame parsing.For each utterance,a specific intent type is generally determined with the indication information of words having slot tags(called as slot words),and in reverse the intent type decides that words of certain categories should be used to fill as slots.However,the Intent-Slot correlation is rarely modeled explicitly in existing studies,and hence may be not fully exploited.In this paper,we model Intent-Slot correlation explicitly and propose a new framework for joint intent prediction and slot filling.Firstly,we explore the effects of slot words on intent by differentiating them from the other words,and we recognize slot words by solving a sequence labeling task with the bi-directional long short-term memory(BiLSTM)model.Then,slot recognition information is introduced into attention-based intent prediction and slot filling to improve semantic results.In addition,we integrate the Slot-Gated mechanism into slot filling to model dependency of slots on intent.Finally,we obtain slot recognition,intent prediction and slot filling by training with joint optimization.Experimental results on the benchmark Air-line Travel Information System(ATIS)and Snips datasets show that our Intent-Slot correlation model achieves state-of-the-art semantic frame performance with a lightweight structure.展开更多
意图识别与语义槽填充联合建模正成为口语理解(Spoken Language Understanding,SLU)的新趋势。但是,现有的联合模型只是简单地将两个任务进行关联,建立了两任务间的单向联系,未充分利用两任务之间的关联关系。考虑到意图识别与语义槽填...意图识别与语义槽填充联合建模正成为口语理解(Spoken Language Understanding,SLU)的新趋势。但是,现有的联合模型只是简单地将两个任务进行关联,建立了两任务间的单向联系,未充分利用两任务之间的关联关系。考虑到意图识别与语义槽填充的双向关联关系可以使两任务相互促进,提出了一种基于门控机制的双向关联模型(BiAss-Gate),将两个任务的上下文信息进行融合,深度挖掘意图识别与语义槽填充之间的联系,从而优化口语理解的整体性能。实验表明,所提模型BiAss-Gate在ATIS和Snips数据集上,语义槽填充F1值最高达95.8%,意图识别准确率最高达98.29%,对比其他模型性能得到了显著提升。展开更多
文摘Slot filling and intent prediction are basic tasks in capturing semantic frame of human utterances.Slots and intent have strong correlation for semantic frame parsing.For each utterance,a specific intent type is generally determined with the indication information of words having slot tags(called as slot words),and in reverse the intent type decides that words of certain categories should be used to fill as slots.However,the Intent-Slot correlation is rarely modeled explicitly in existing studies,and hence may be not fully exploited.In this paper,we model Intent-Slot correlation explicitly and propose a new framework for joint intent prediction and slot filling.Firstly,we explore the effects of slot words on intent by differentiating them from the other words,and we recognize slot words by solving a sequence labeling task with the bi-directional long short-term memory(BiLSTM)model.Then,slot recognition information is introduced into attention-based intent prediction and slot filling to improve semantic results.In addition,we integrate the Slot-Gated mechanism into slot filling to model dependency of slots on intent.Finally,we obtain slot recognition,intent prediction and slot filling by training with joint optimization.Experimental results on the benchmark Air-line Travel Information System(ATIS)and Snips datasets show that our Intent-Slot correlation model achieves state-of-the-art semantic frame performance with a lightweight structure.
文摘意图识别与语义槽填充联合建模正成为口语理解(Spoken Language Understanding,SLU)的新趋势。但是,现有的联合模型只是简单地将两个任务进行关联,建立了两任务间的单向联系,未充分利用两任务之间的关联关系。考虑到意图识别与语义槽填充的双向关联关系可以使两任务相互促进,提出了一种基于门控机制的双向关联模型(BiAss-Gate),将两个任务的上下文信息进行融合,深度挖掘意图识别与语义槽填充之间的联系,从而优化口语理解的整体性能。实验表明,所提模型BiAss-Gate在ATIS和Snips数据集上,语义槽填充F1值最高达95.8%,意图识别准确率最高达98.29%,对比其他模型性能得到了显著提升。
