Learning Low-Resource End-To-End Goal-Oriented Dialog for Fast and Reliable System Deployment

Dai, Yinpei; Li, Hangyu; Tang, Chengguang; Li, Yongbin; Sun, Jian; Zhu, Xiaodan

doi:10.18653/v1/2020.acl-main.57

Cited by 25 publications

(18 citation statements)

References 18 publications

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“…By selectively overwriting the memory module, they improved the efficiency of the dialogue state tracking task. Dai et al (2020) applied the MemN2N (Sukhbaatar et al, 2015) as task-oriented utterance encoder, memorizing the existing responses and dialogue history. Then they used model-agnostic meta-learning (MAML) (Finn et al, 2017) to train the framework to retrieve correct responses in a few-shot fashion.…”

Section: Memory Networkmentioning

confidence: 99%

Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Survey

Ni¹,

Young²,

Pandelea³

et al. 2021

Preprint

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Dialogue systems are a popular Natural Language Processing (NLP) task as it is promising in real-life applications. It is also a complicated task since many NLP tasks deserving study are involved. As a result, a multitude of novel works on this task are carried out, and most of them are deep learning based due to the outstanding performance. In this survey, we mainly focus on the deep learning based dialogue systems. We comprehensively review state-of-the-art research outcomes in dialogue systems and analyze them from two angles: model type and system type. Specifically, from the angle of model type, we discuss the principles, characteristics, and applications of different models that are widely used in dialogue systems. This will help researchers acquaint these models and see how they are applied in state-of-the-art frameworks, which is rather helpful when designing a new dialogue system. From the angle of system type, we discuss task-oriented and open-domain dialogue systems as two streams of research, providing insight into the hot topics related. Furthermore, we comprehensively review the evaluation methods and datasets for dialogue systems to pave the way for future research. Finally, some possible research trends are identified based on the recent research outcomes. To the best of our knowledge, this survey is the most comprehensive and up-to-date one at present in the area of dialogue systems and dialogue-related tasks, extensively covering the popular frameworks, topics, and datasets 1 . * Equal contribution ‡ Corresponding author 1 The frameworks, topics, and datasets discussed are originated from the extensive literature review of state-of-the-art research. We have tried our best to cover all but may still omit some works. Readers are welcome to provide suggestions regarding the omissions and mistakes in this article. We also intend to update this article with time as and when new approaches or definitions are proposed and used by the community Preprint. Under review.

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Section: Memory Networkmentioning

confidence: 99%

Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Survey

Ni¹,

Young²,

Pandelea³

et al. 2021

Preprint

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“…Huang et al (2018); Gu et al (2018); Sennrich and Zhang (2019); Bansal et al (2019); Dou et al (2019); Yan et al (2020) attempt at using meta-learning for efficient transfer of knowledge from high-resource tasks to a low-resource task. Further, some of the more recent works (Dai et al, 2020;Qian and Yu, 2019) have shown meta-learners can be used for system response generation in TOD systems which is generally downstream task for our DST task.…”

Section: Introductionmentioning

confidence: 99%

Few Shot Dialogue State Tracking using Meta-learning

Dingliwal¹,

Gao²,

Agarwal³

et al. 2021

Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

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Dialogue State Tracking (DST) forms a core component of automated chatbot based systems designed for specific goals like hotel, taxi reservation, tourist information etc. With the increasing need to deploy such systems in new domains, solving the problem of zero/fewshot DST has become necessary. There has been a rising trend for learning to transfer knowledge from resource-rich domains to unknown domains with minimal need for additional data. In this work, we explore the merits of meta-learning algorithms for this transfer and hence, propose a meta-learner D-REPTILE specific to the DST problem. With extensive experimentation, we provide clear evidence of benefits over conventional approaches across different domains, methods, base models and datasets with significant (5-25%) improvement over the baseline in lowdata setting. Our proposed meta-learner is agnostic of the underlying model and hence any existing state-of-the-art DST system can improve its performance on unknown domains using our training strategy.

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“…One of the long-standing goals of natural language processing (NLP) is to enable machines to have the ability to understand natural language and make inferences in textual data. Many applications, such as dialogue systems [1,2], recommendation systems [3][4][5], question answering [6,7], and sentiment analysis [8], aim to explore the machine ability to understand textual data. Question answering, abbreviated as QA, has emerged as an important natural language processing task because it provides a quantifiable way to evaluate an NLP system's capability on language understanding and reasoning and its commercial value for real-world applications.…”

Section: Introductionmentioning

confidence: 99%

Dual‐Channel Reasoning Model for Complex Question Answering

Cao

Liu

et al. 2021

Complexity

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Multihop question answering has attracted extensive studies in recent years because of the emergence of human annotated datasets and associated leaderboards. Recent studies have revealed that question answering systems learn to exploit annotation artifacts and other biases in current datasets. Therefore, a model with strong interpretability should not only predict the final answer, but more importantly find the supporting facts’ sentences necessary to answer complex questions, also known as evidence sentences. Most existing methods predict the final answer and evidence sentences in sequence or simultaneously, which inhibits the ability of models to predict the path of reasoning. In this paper, we propose a dual-channel reasoning architecture, where two reasoning channels predict the final answer and supporting facts’ sentences, respectively, while sharing the contextual embedding layer. The two reasoning channels can simply use the same reasoning structure without additional network designs. Through experimental analysis based on public question answering datasets, we demonstrate the effectiveness of our proposed method

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Learning Low-Resource End-To-End Goal-Oriented Dialog for Fast and Reliable System Deployment

Cited by 25 publications

References 18 publications

Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Survey

Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Survey

Few Shot Dialogue State Tracking using Meta-learning

Dual‐Channel Reasoning Model for Complex Question Answering

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