| Challenge: | Current dialog systems require human experts to design the dialog structure, which is time consuming and sometimes insufficient to satisfy various customer needs. |
| Approach: | They propose to extract dialog structure using a modified VRNN model with discrete latent vectors. |
| Outcome: | The proposed model outperforms existing models on the ability to predict unseen data and is faster and more effective in a reinforcement learning setting. |
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Discovering Dialog Structure Graph for Coherent Dialog Generation (2021.acl-long)
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| Challenge: | Existing studies on dialog structure graphs from open-domain dialogs have limited number of dialog states and can be laborious and costly to annotate manually. |
| Approach: | They propose to use dialog structure graph as a model to discover hierarchical latent dialog states and their transitions from corpus to facilitate dialog management in a RL based dialog system. |
| Outcome: | The proposed model can discover meaningful dialog structure graph and significantly improve multi-turn coherence on two benchmark corpora. |
Unsupervised Discrete Sentence Representation Learning for Interpretable Neural Dialog Generation (P18-1)
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| Challenge: | Existing encoder-decoder dialog models cannot output interpretable actions as in traditional systems. |
| Approach: | They propose an unsupervised discrete sentence representation learning method that integrates with existing encoder-decoder dialog models for interpretable response generation. |
| Outcome: | The proposed model can be integrated with existing encoder-decoder dialog models and discover interpretable semantics via either auto encoding or context predicting. |
Structured Attention for Unsupervised Dialogue Structure Induction (2020.emnlp-main)
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| Challenge: | Using structured attention, a model can learn dialogue structure in unsupervised fashion. |
| Approach: | They propose to incorporate structured attention layers into a Variational Recurrent Neural Network model with discrete latent states to learn dialogue structure in an unsupervised fashion. |
| Outcome: | The proposed model learns semantic structures similar to templates used to generate a dialogue corpus on two-party datasets and on multi-party dialogues, disentangling dialogues without human annotation. |
DialogVED: A Pre-trained Latent Variable Encoder-Decoder Model for Dialog Response Generation (2022.acl-long)
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Wei Chen, Yeyun Gong, Song Wang, Bolun Yao, Weizhen Qi, Zhongyu Wei, Xiaowu Hu, Bartuer Zhou, Yi Mao, Weizhu Chen, Biao Cheng, Nan Duan
| Challenge: | Existing pre-trained dialog models shed light on various downstream tasks in natural language processing (NLP). |
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| Outcome: | The proposed model achieves state-of-the-art on personaChat, DailyDialog, and DSTC7-AVSD datasets. |
A Hierarchical Latent Structure for Variational Conversation Modeling (N18-1)
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| Challenge: | Variational autoencoders suffer from the notorious degeneration problem, according to a new study . utterance drop regularization is an important feature of the hierarchical RNNs . |
| Approach: | They propose a variational hierarchical conversation RNN framework that exploits latent variables and an utterance drop regularization to exploit latent variable. |
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Towards a Zero-Data, Controllable, Adaptive Dialog System (2024.lrec-main)
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| Challenge: | Recent approaches to controllable dialog systems require additional training data to be deployed in new domains. |
| Approach: | They propose to generate dialog tree data directly from dialog trees by using a commercial Large Language Model or a single GPU. |
| Outcome: | The proposed approach can achieve comparable dialog success to models trained on human data. |
Variational Hierarchical Dialog Autoencoder for Dialog State Tracking Data Augmentation (2020.emnlp-main)
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| Challenge: | Recent studies have shown that generative data augmentation, where synthetic samples generated from deep generative models complement the training dataset, benefit NLP tasks. |
| Approach: | They propose a Variational Hierarchical Dialog Autoencoder for modeling the complete aspects of goal-oriented dialogs using inter-connected latent variables and learns to generate coherent dialogs from the latent spaces. |
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Unsupervised Learning of Syntactic Structure with Invertible Neural Projections (D18-1)
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| Challenge: | Unsupervised learning of syntactic structure is typically performed using generative models with discrete latent variables and multinomial parameters. |
| Approach: | They propose a generative model that jointly learns discrete syntactic structure and continuous word representations in an unsupervised fashion by cascading an invertible neural network with a structured generative prior. |
| Outcome: | The proposed model outperforms state-of-the-art models on part-of speech (POS) induction and unsupervised dependency parsing without gold POS annotation. |
End-to-End Learning of Task-Oriented Dialogs (N18-4)
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| Challenge: | Dissertation addresses the limitations of conventional task-oriented dialog systems . conventions of such systems include a complex pipeline and dialog state tracking . |
| Approach: | They propose a neural network based dialog system that can robustly track dialog state . they propose offline training and online interactive learning methods to improve efficiency . |
| Outcome: | The proposed system can track dialog state, interface with knowledge bases, and integrate structured query results into system responses to successfully complete task-oriented dialog. |
Learning Interpretable Latent Dialogue Actions With Less Supervision (2022.aacl-main)
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| Challenge: | supervised neural dialogue modeling requires a significant amount of work to obtain turn-level labels, usually with dialogue state annotation. |
| Approach: | They propose a novel architecture for explainable modeling of task-oriented dialogues with discrete latent variables to represent dialogue actions. |
| Outcome: | The proposed model outperforms previous approaches with less supervision in terms of perplexity and BLEU on three datasets. |