A Simple Language Model for Task-Oriented Dialogue

Hosseini-Asl, Ehsan; McCann, Bryan; Wu, Chien-Sheng; Yavuz, Semih; Socher, Richard

doi:10.48550/arxiv.2005.00796

Cited by 25 publications

(56 citation statements)

References 34 publications

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“…Reinforcement learning [18,58,80] and powerful transformers such as GPT-2 [29,49] have been used to optimize the dialog system. These methods typically require a large task-specific corpus to train policy over conversational actions, while our method only needs few interactions with humans for a new task, and our policies can be extended to actions beyond conversational ones.…”

Section: Natural Language Processing: Dialog Systemsmentioning

confidence: 99%

Reinforced Natural Language Interfaces via Entropy Decomposition

Wu¹

2021

Preprint

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In this paper, we study the technical problem of developing conversational agents that can quickly adapt to unseen tasks, learn task-specific communication tactics, and help listeners finish complex, temporally extended tasks. We find that the uncertainty of language learning can be decomposed to an entropy term and a mutual information term, corresponding to the structural and functional aspect of language, respectively. Combined with reinforcement learning, our method automatically requests human samples for training when adapting to new tasks and learns communication protocols that are succinct and helpful for task completion. Human and simulation test results on a referential game and a 3D navigation game prove the effectiveness of the proposed method.CCS Concepts: • Human-centered computing → Natural language interfaces.

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Section: Natural Language Processing: Dialog Systemsmentioning

confidence: 99%

Reinforced Natural Language Interfaces via Entropy Decomposition

Wu¹

2021

Preprint

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“…Annotation error Even the recent versions of MultiWOZ still have incorrect labels and inconsistent annotations [3,21,4,20,5]. These noises are the primary reason why it is challenging to accurately evaluate the model performance.…”

Section: Data Limitationmentioning

confidence: 99%

Oh My Mistake!: Toward Realistic Dialogue State Tracking including Turnback Utterances

Takyoung¹,

Lee²,

Yoon³

et al. 2021

Preprint

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The primary purpose of dialogue state tracking (DST), a critical component of an end-to-end conversational system, is to build a model that responds well to real-world situations. Although we often change our minds during ordinary conversations, current benchmark datasets do not adequately reflect such occurrences and instead consist of over-simplified conversations, in which no one changes their mind during a conversation. As the main question inspiring the present study,"Are current benchmark datasets sufficiently diverse to handle casual conversations in which one changes their mind?" We found that the answer is "No" because simply injecting template-based turnback utterances significantly degrades the DST model performance. The test joint goal accuracy on the MultiWOZ decreased by over 5%p when the simplest form of turnback utterance was injected. Moreover, the performance degeneration worsens when facing more complicated turnback situations. However, we also observed that the performance rebounds when a turnback is appropriately included in the training dataset, implying that the problem is not with the DST models but rather with the construction of the benchmark dataset.Preprint. Under review.

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“…These systems are the core modules of virtual assistants (e.g., Apple Siri and Amazon Alexa), and they provide natural language interfaces for online services [1]. Recently, there has been growing interest in developing deep learning-based end-to-end ToD systems [2,3,4,5,6,7,8,9,10,11,12,13,14,15,16] because they can handle complex dialogue patterns with minimal hand-crafted rules. To advance the existing state-of-the-art, large-scale datasets [17,1,16] have been proposed for training and evaluating such data-driven systems.…”

Section: Introductionmentioning

confidence: 99%

BiToD: A Bilingual Multi-Domain Dataset For Task-Oriented Dialogue Modeling

Lin,

Madotto,

Winata

et al. 2021

Preprint

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Task-oriented dialogue (ToD) benchmarks provide an important avenue to measure progress and develop better conversational agents. However, existing datasets for end-to-end ToD modeling are limited to a single language, hindering the development of robust end-to-end ToD systems for multilingual countries and regions. Here we introduce BiToD 2 , the first bilingual multi-domain dataset for end-to-end task-oriented dialogue modeling. BiToD contains over 7k multi-domain dialogues (144k utterances) with a large and realistic bilingual knowledge base. It serves as an effective benchmark for evaluating bilingual ToD systems and crosslingual transfer learning approaches. We provide state-of-the-art baselines under three evaluation settings (monolingual, bilingual, and cross-lingual). The analysis of our baselines in different settings highlights 1) the effectiveness of training a bilingual ToD system compared to two independent monolingual ToD systems, and 2) the potential of leveraging a bilingual knowledge base and cross-lingual transfer learning to improve the system performance under low resource conditions. * Equal contribution 2 Data and code are available in https://github.com/HLTCHKUST/BiToD.Preprint. Under review.

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A Simple Language Model for Task-Oriented Dialogue

Cited by 25 publications

References 34 publications

Reinforced Natural Language Interfaces via Entropy Decomposition

Reinforced Natural Language Interfaces via Entropy Decomposition

Oh My Mistake!: Toward Realistic Dialogue State Tracking including Turnback Utterances

BiToD: A Bilingual Multi-Domain Dataset For Task-Oriented Dialogue Modeling

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