| Challenge: | Dialogue state tracking is a key component of dialogue systems. |
| Approach: | They propose to extend the definition of dialogue state tracking to multimodality . they propose a new synthetic benchmark and a novel baseline for this task . |
| Outcome: | The proposed task is based on a synthetic benchmark and a self-supervised video understanding task. |
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Scaling Multi-Domain Dialogue State Tracking via Query Reformulation (N19-2)
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| Challenge: | Using a pointer-generator network, we model the reference resolution task as a dialogue context-aware user query reformulation task. |
| Approach: | They propose a pointer-generator network and a novel multi-task learning setup to model dialogue state tracking and referring expression resolution tasks using a dialogue context-aware user query reformulation task. |
| Outcome: | The proposed model improves absolute F1 on internal and public benchmarks. |
Multimodal Transformer Networks for End-to-End Video-Grounded Dialogue Systems (P19-1)
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| Challenge: | Existing work on video-grounded dialogue systems is limited by feature space and semantic information. |
| Approach: | They propose multimodal transformer networks to encode videos and incorporate information from different modalities. |
| Outcome: | The proposed system generates appropriate conversational response to queries of humans based on visual and audio aspects of a given video . it also generalizes to another multimodal visual-grounded dialogue task, and obtains promising performance. |
Dialogue State Tracking with Incremental Reasoning (2021.tacl-1)
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| Challenge: | Empirical results show that our method outperforms the state-of-the-art methods in terms of joint belief accuracy. |
| Approach: | They propose to track dialogue states gradually with reasoning over dialogue turns using the back-end data. |
| Outcome: | Empirical results show that the proposed method outperforms state-of-the-art methods in terms of joint belief accuracy for a large-scale human–human dialogue dataset. |
Scalable and Accurate Dialogue State Tracking via Hierarchical Sequence Generation (D19-1)
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| Challenge: | Existing approaches to dialogue state tracking rely on pre-defined ontologies . however, these methods suffer from computational complexity that increases proportionally to the number of pre-determined slots. |
| Approach: | They propose a model that generates a sequence of belief states without the pre-defined ontology list. |
| Outcome: | The proposed model scales easily with the increasing number of pre-defined slots and domains and reaches the state-of-the-art performance on the multi-domain and single domain dialogue state tracking datasets. |
Towards Universal Dialogue State Tracking (D18-1)
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| Challenge: | Existing approaches to dialogue state tracking are difficult to scale to large dialogue domains. |
| Approach: | They propose a universal dialogue state tracker that is independent of the number of values and shares parameters across all slots. |
| Outcome: | The proposed system significantly outperforms state-of-the-art approaches on two datasets. |
Beyond the Granularity: Multi-Perspective Dialogue Collaborative Selection for Dialogue State Tracking (2022.acl-long)
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| Challenge: | Experimental results show that task-oriented dialogue systems have attracted growing attention and achieved substantial progress. |
| Approach: | They propose a method that dynamically selects relevant dialogue contents for each slot . they retrieve turn-level utterances and evaluate their relevance to the slot from three perspectives . |
| Outcome: | The proposed method achieves state-of-the-art performance on MultiWOZ 2.1 and MultiWOz 2.2 and superior performance on multiple mainstream benchmark datasets. |
Multi-Domain Dialogue State Tracking By Neural-Retrieval Augmentation (2022.findings-aacl)
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| Challenge: | Existing approaches for DST are conditioned on previous dialogue states, but the dependency on previous dialogs makes it difficult to prevent error propagation to subsequent turns. |
| Approach: | They propose to create a Neural Index based on dialogue context by analyzing user dialogue and previous turn state and generating a retrieval-guided generation approach. |
| Outcome: | The proposed framework retrieves dialogue context from the index built using unstructured dialogue state and structured user/system utterances. |
A Sequence-to-Sequence Approach to Dialogue State Tracking (2021.acl-long)
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| Challenge: | Existing methods for dialogue state tracking are still challenging, but they are improving . a new approach to dialogue state monitoring is proposed, called Seq2Seq-DU . |
| Approach: | They propose a new dialogue state tracking module that formalizes DST as a sequence-to-sequence problem. |
| Outcome: | The proposed method outperforms existing methods on benchmark datasets in different settings. |
Ordinal and Attribute Aware Response Generation in a Multimodal Dialogue System (P19-1)
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| Challenge: | Existing multimodal dialogue systems are based on unimodal sources, capturing information from text and image. |
| Approach: | They propose a position and attribute aware attention mechanism to learn enhanced image representation conditioned on the user utterance. |
| Outcome: | The proposed model outperforms the state-of-the-art models on text similarity metrics. |
Knowledge-Aware Graph-Enhanced GPT-2 for Dialogue State Tracking (2021.emnlp-main)
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| Challenge: | Existing models for dialogue state tracking are based on Graph Attention Networks . if the relationship between slots and values is modelled explicitly, this can be improved . |
| Approach: | They propose a model architecture that augments GPT-2 with Graph Attention Networks to allow sequential prediction of slot values. |
| Outcome: | The proposed architecture improves performance against a strong GPT-2 baseline and with sparsely supervised training. |