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natural-language-processing-7

Dialogue State Tracking

3260 papers • 126 benchmarks • 313 datasets

Dialogue state tacking consists of determining at each turn of a dialogue the full representation of what the user wants at that point in the dialogue, which contains a goal constraint, a set of requested slots, and the user's dialogue act.

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Benchmarks

These leaderboards are used to track progress in dialogue-state-tracking-14

Trend

Dataset

Best Model

Actions

Wizard-of-Oz

Wizard-of-Oz

CoSQL

CoSQL

Second dialogue state tracking challenge

Libraries

i

Use these libraries to find dialogue-state-tracking-14 models and implementations

budzianowski/multiwoz

3 papers 818

Datasets

MultiWOZ

SGD

CoSQL

Dialogue State Tracking Challenge

Dialogue State Tracking Challenge

Wizard-of-Oz

CrossWOZ

Subtasks

No subtasks available.

Most implemented papers

Towards Scalable Multi-domain Conversational Agents: The Schema-Guided Dialogue Dataset

Xiaoxue Zang, Abhinav Rastogi, Srinivas Sunkara, Raghav Gupta, Pranav Khaitan•Wed Sep 11 2019

This work introduces the the Schema-Guided Dialogue (SGD) dataset, containing over 16k multi-domain conversations spanning 16 domains, and presents a schema-guided paradigm for task-oriented dialogue, in which predictions are made over a dynamic set of intents and slots provided as input.

679

Content

Introduction Benchmarks Datasets Subtasks Libraries Papers

Second dialogue state tracking challenge

SIMMC2.0

SIMMC2.0

MULTIWOZ 2.1

MULTIWOZ 2.1

MULTIWOZ 2.2

MULTIWOZ 2.2

MMConv

MMConv

google-research-datasets/simulated-…

2 papers 226

KLUE

Taskmaster-1

SIMMC2.0

RiSAWOZ

0

Editing-Based SQL Query Generation for Cross-Domain Context-Dependent Questions

R. Socher, Caiming Xiong, Tao Yu, Rui Zhang, Xi Victoria Lin, H. Er, Sungrok Shim, Dragomir R. Radev, Eric Xue, Tianze Shi•Sat Aug 31 2019

The interaction history is utilized by editing the previous predicted query to improve the generation quality of SQL queries and the benefit of editing compared with the state-of-the-art baselines which generate SQL from scratch is evaluated.

156 0

CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases

Michihiro Yasunaga, R. Socher, Caiming Xiong, Luyao Chen, Tao Yu, Rui Zhang, Y. Tan, Xi Victoria Lin, Suyi Li, H. Er, B. Pang, Tao Chen, Shreya Dixit, Sungrok Shim, Vincent Zhang, Dragomir R. Radev, Alexander R. Fabbri, Eric Xue, Tianze Shi, Zihan Li, Youxuan Jiang, Zifan Li, Yuwen Zhang, Walter S. Lasecki•Tue Sep 10 2019

CoSQL is presented, a corpus for building cross-domain, general-purpose database (DB) querying dialogue systems that includes SQL-grounded dialogue state tracking, response generation from query results, and user dialogue act prediction and a set of strong baselines are evaluated.

270 0

Efficient Dialogue State Tracking by Selectively Overwriting Memory

Sang-Woo Lee, Sohee Yang, Sungdong Kim, Gyuwan Kim•Sat Nov 09 2019

The accuracy gaps between the current and the ground truth-given situations are analyzed and it is suggested that it is a promising direction to improve state operation prediction to boost the DST performance.

202 0

MultiWOZ 2.3: A Multi-domain Task-Oriented Dialogue Dataset Enhanced with Annotation Corrections and Co-Reference Annotation

Minlie Huang, Wei Peng, Ting Han, Ximing Liu, Ryuichi Takanobu, Yixin Lian, Chongxuan Huang, Dazhen Wan•Sun Oct 11 2020

This paper introduces MultiWOZ 2.3, in which it differentiate incorrect annotations in dialogue acts from dialogue states, and identifies a lack of co-reference when publishing the updated dataset, to ensure consistency between dialogue acts and dialogue states.

70 0

KLUE: Korean Language Understanding Evaluation

Alice H. Oh, Sungjoon Park, Hyunwoo Kim, Jung-Woo Ha, Jihyung Moon, Won Ik Cho, Sungdong Kim, Kyunghyun Cho, Jiyoon Han, Jangwon Park, Chisung Song, Junseong Kim, Yongsook Song, Tae Hwan Oh, Joohong Lee, Juhyun Oh, Sungwon Lyu, Young-kuk Jeong, I. Lee, Sang-gyu Seo, Myeonghwa Lee, Seongbo Jang, Seungwon Do, SunKyoung Kim, Kyungtae Lim, Jongwon Lee, Kyumin Park, Jamin Shin, Seonghyun Kim, Lucy Park•Wed May 19 2021

This work introduces Korean Language Understanding Evaluation (KLUE), a collection of 8 Korean natural language understanding (NLU) tasks, including Topic Classification, SemanticTextual Similarity, Natural Language Inference, Named Entity Recognition, Relation Extraction, Dependency Parsing, Machine Reading Comprehension, and Dialogue State Tracking, and provides suitable evaluation metrics and fine-tuning recipes for pretrained language models for each task.

221 0

Semantic Specialisation of Distributional Word Vector Spaces using Monolingual and Cross-Lingual Constraints

A. Korhonen, N. Mrksic, Diarmuid Ó Séaghdha, S. Young, Ivan Vulic, Milica Gasic, Roi Reichart, Ira Leviant•Wed May 31 2017

The evaluation shows that the Attract-Repel method can make use of existing cross-lingual lexicons to construct high-quality vector spaces for a plethora of different languages, facilitating semantic transfer from high- to lower-resource ones.

221 0

Counter-fitting Word Vectors to Linguistic Constraints

N. Mrksic, Diarmuid Ó Séaghdha, Tsung-Hsien Wen, Blaise Thomson, S. Young, Pei-hao Su, L. Rojas-Barahona, Milica Gasic, David Vandyke•Tue Mar 01 2016

A novel counter-fitting method is presented which injects antonymy and synonymy constraints into vector space representations in order to improve the vectors' capability for judging semantic similarity.

505 0

Adding a benchmark result helps the community track progress.

Dialogue State Tracking | State-of-the-Art