Papers with task-oriented dialog
What we need to learn if we want to do and not just talk (N18-3)
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| Challenge: | Existing methods for task-oriented dialogs require fluent natural language responses and correct external actions . but they perform poorly in real world dialog tasks, a new study shows . |
| Approach: | They propose a hybrid model where nearest neighbor is used to generate fluent responses and Seq2Seq type models ensure dialogue coherency and generate accurate external actions. |
| Outcome: | The proposed model achieves a 78% relative improvement in fluency and 200% improvement in accuracy of external calls. |
CGIM: A Cycle Guided Interactive Learning Model for Consistency Identification in Task-oriented Dialogue (2022.coling-1)
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| Challenge: | Consistency identification in task-oriented dialog usually consists of three subtasks . a proposed model for consistency identification in dialog is based on an explicit interaction paradigm . |
| Approach: | They propose a cycle guided interactive learning model that makes information exchange explicit from all the three tasks. |
| Outcome: | The proposed model achieves state-of-the-art performance pushing the overall score to 56.3% (5.0% point absolute improvement) |
Multi-Agent Task-Oriented Dialog Policy Learning with Role-Aware Reward Decomposition (2020.acl-main)
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| Challenge: | Many studies have applied reinforcement learning to train a dialog policy . but modeling a real-world user simulator is challenging and requires domain expertise . |
| Approach: | They propose to build dialog policies with two agents as dialog agents to avoid building a user simulator beforehand. |
| Outcome: | The proposed method can build a system policy and a user policy simultaneously . it can achieve high task success rate through conversational interaction . |
Multi 3 WOZ: A Multilingual, Multi-Domain, Multi-Parallel Dataset for Training and Evaluating Culturally Adapted Task-Oriented Dialog Systems (2023.tacl-1)
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Songbo Hu, Han Zhou, Mete Hergul, Milan Gritta, Guchun Zhang, Ignacio Iacobacci, Ivan Vulić, Anna Korhonen
| Challenge: | Task-oriented dialog (TOD) is one of the central objectives, hallmarks, and applications of machine intelligence. |
| Approach: | They propose a multilingual, multi-domain, multiparallele ToD dataset that offers culturally adapted dialogs in 4 languages for training and evaluation of multilingual and cross-lingual systems. |
| Outcome: | The proposed dataset is large-scale and culturally adapted to enable training and evaluation of multilingual and cross-lingual ToD systems. |
The Devil is in the Details: On Models and Training Regimes for Few-Shot Intent Classification (2023.eacl-main)
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| Challenge: | Recent methods for task-oriented dialog (ToD) intent classification use pretrained language models . but lack of informative ablations prevents identification of factors that drive performance . |
| Approach: | They propose a framework to evaluate components of Few-Shot Intent Classification . they propose to combine cross-encoder architecture and episodic meta-learning . |
| Outcome: | The proposed framework evaluates cross-encoder architecture and episodic meta-learning . it also shows that splitting episodes into support and query sets outperforms non-episodic counterparts. |
Self-training Improves Pre-training for Few-shot Learning in Task-oriented Dialog Systems (2021.emnlp-main)
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| Challenge: | Large-scale pre-trained language models have shown promising results for few-shot learning in task-oriented dialog (ToD) systems. |
| Approach: | They propose a self-training approach that iteratively labels the most confident unlabeled data to train a stronger Student model. |
| Outcome: | The proposed approach improves state-of-the-art pre-trained models in few-shot learning scenarios for task-oriented dialog (ToD) systems when only a small number of labeled data are available. |
Robustness Testing of Language Understanding in Task-Oriented Dialog (2021.acl-long)
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Jiexi Liu, Ryuichi Takanobu, Jiaxin Wen, Dazhen Wan, Hongguang Li, Weiran Nie, Cheng Li, Wei Peng, Minlie Huang
| Challenge: | a lack of systematic studies on the robustness of language understanding models in task-oriented dialog systems is limiting . authors propose a model-agnostic toolkit LAUG to approximate natural language perturbations . |
| Approach: | They propose a model-agnostic toolkit LAUG to approximate natural language perturbations for testing the robustness of language understanding models in task-oriented dialog systems. |
| Outcome: | The proposed toolkit reveals critical robustness issues in state-of-the-art models. |