MultiWOZ 2.

By Mihail Eric, Rahul Goel, Shachi Paul, Adarsh Kumar, Adarsh Kumar, Abhishek Sethi, Anuj Kumar Goyal, Peter Ku, Sanchit Agarwal, Shuyang Gao. 2019

Subfields: Natural Language Processing, Evaluation & Benchmarks

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Citations in the corpus

Semantic Noise Matters for Neural Natural Language Generation (2019, background): “These ﬁndings suggest that efforts to create more accurate training data—whether through stricter crowd-sourcing protocols, conducting follow-up annotations (cf. Eric et al., 2019), or automated cleanup heuristics like we report here—are likely necessary in the NLG and dialogue systems communities.”
GLM-130B: An Open Bilingual Pre-trained Model (2022, methodology): “1 (Eric et al., 2020) dialogue state tracking dataset.”
ConvLab-2: An Open-Source Toolkit for Building, Evaluating, and Diagnosing Dialogue Systems (2020, methodology): “This section demonstrates how to use ConvLab2 to build, evaluate, and diagnose a traditional pipeline dialogue system developed on the MultiWOZ dataset.”
Domain-independent User Simulation with Transformers for Task-oriented Dialogue Systems (2021, methodology): “We train our model 4 on the MultiWOZ 2.1 dataset (Eric et al., 2020), consisting of dialogues between two humans, one posing as a user and the other as an operator.”
Zero-Shot Transfer Learning with Synthesized Data for Multi-Domain Dialogue State Tracking (2020, methodology): “The MultiWOZ dataset (Budzianowski et al., 2018; Eric et al., 2019) is a multi-domain fullylabeled corpus of human-human written conversations.”
Language Models as Few-Shot Learner for Task-Oriented Dialogue Systems (2020, methodology): “ACT We use the MultiWoZ dataset for evaluating the speech ACT identification task.”
MultiWOZ 2.2 : A Dialogue Dataset with Additional Annotation Corrections and State Tracking Baselines (2020, background): “MultiWOZ 2.1 (Eric et al., 2019) is an improved version of the MultiWOZ 2.0 dataset, which is extensively used as a benchmark for dialogue state tracking.”
Uncertainty Measures in Neural Belief Tracking and the Effects on Dialogue Policy Performance (2021, background): “We ﬁrst investigate which states to use for the supervised pretraining (Section 3.3): oracle states, i.e., the dialogue state labels from the MultiWOZ corpus, or estimated belief states, e.g., those predicted by a EnD-SetSUMBT model.”
Efficient Retrieval Augmented Generation from Unstructured Knowledge for Task-Oriented Dialog (2021, methodology): “The training data provided as part of the challenge for this task is based on the MultiWOZ 2.1 dataset (Budzianowski et al. 2018; Eric et al. 2020).”
Conversation Graph: Data Augmentation, Training, and Evaluation for Non-Deterministic Dialogue Management (2020, methodology): “Note that the original dataset has since been corrected and released as MultiWOZ 2.1 (Eric et al., 2020).”
Preview, Attend and Review: Schema-Aware Curriculum Learning for Multi-Domain Dialogue State Tracking (2021, background): “There are also many works (Hou et al., 2018; Yoo et al., 2020; Yin et al., 2020) on dialog augmentation.”
TripPy: A Triple Copy Strategy for Value Independent Neural Dialog State Tracking (2020, methodology): “Current state-of-the-art has to prove to work on long, diverse conversations in multiple domains with a high slot count and principally unrestricted vocabulary (Eric et al., 2019).”
MinTL: Minimalist Transfer Learning for Task-Oriented Dialogue Systems (2020, methodology): “However, the recently proposed multi-domain task-oriented dialogue datasets (Budzianowski et al., 2018; Eric et al., 2019) bring new challenges for multi-domain dialogue state tracking and response generation.”
DeepStruct: Pretraining of Language Models for Structure Prediction (2022, methodology): “Datasets We use the MultiWOZ 2.1 (Budzianowski et al., 2018; Ramadan et al., 2018; Eric et al., 2020; Zang et al., 2020), which is a daily dialogue dataset for task-oriented conversations.”