natural-language-processing-7

Goal-Oriented Dialogue Systems

3260 papers • 126 benchmarks • 313 datasets

Achieving a pre-defined goal through a dialog.

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Datasets

MultiDoc2Dial

MMD

DSTC7 Task 1

MetaLWOz

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Most implemented papers

Rethinking Action Spaces for Reinforcement Learning in End-to-end Dialog Agents with Latent Variable Models

M. Eskénazi, Tiancheng Zhao, Kaige Xie•Thu Jan 31 2019

A novel latent action framework that treats the action spaces of an end-to-end dialog agent as latent variables and develops unsupervised methods in order to induce its own action space from the data is proposed.

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Utterance-level Dialogue Understanding: An Empirical Study

Soujanya Poria, Navonil Majumder, Deepanway Ghosal, Rada Mihalcea•Mon Sep 28 2020

This paper explores and quantify the role of context for different aspects of a dialogue, namely emotion, intent, and dialogue act identification, using state-of-the-art dialog understanding methods as baselines and employs various perturbations to distort the context of a given utterance and study its impact on the different tasks and baselines.

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In-Context Learning User Simulators for Task-Oriented Dialog Systems

Silvia Terragni, Modestas Filipavicius, Nghia Khau, Bruna Guedes, A. Manso, Roland Mathis•Wed May 31 2023

A novel application of large language models in user simulation for task-oriented dialog systems, specifically focusing on an in-context learning approach, which eliminates the need for labor-intensive rule definition or extensive annotated data, making it more efficient and accessible.

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Sequential Dialogue Context Modeling for Spoken Language Understanding

Larry Heck, Dilek Z. Hakkani-Tür, Gökhan Tür, Ankur Bapna•Sun May 07 2017

A novel approach for modeling dialogue context in a recurrent neural network (RNN) based language understanding system that allows encoding context from the dialogue history in chronological order and results in reduced semantic frame error rates.

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NIPS Conversational Intelligence Challenge 2017 Winner System: Skill-based Conversational Agent with Supervised Dialog Manager

Idris Yusupov, Yuri Kuratov•Tue Jul 31 2018

This work presents bot#1337, a dialog system developed for the 1st NIPS Conversational Intelligence Challenge 2017 (ConvAI), which won the competition with an average dialogue quality score of 2.78 out of 5 given by human evaluators.

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Incorporating Joint Embeddings into Goal-Oriented Dialogues with Multi-task Learning

Jens Lehmann, Debanjan Chaudhuri, Firas Kassawat•Sat Jun 01 2019

An RNN-based end-to-end encoder-decoder architecture which is trained with joint embeddings of the knowledge graph and the corpus as input and incorporates a Knowledge Graph entity lookup during inference to guarantee the generated output is state-full based on the local knowledge graph provided.

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Grounding Dialogue Systems via Knowledge Graph Aware Decoding with Pre-trained Transformers

Jens Lehmann, Md. Rashad Al Hasan Rony, Debanjan Chaudhuri•Mon Mar 29 2021

A novel architecture for integrating KGs into the response generation process by training a BERT model that learns to answer using the elements of the KG (entities and relations) in a multi-task, end-to-end setting is proposed.

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On the Robustness of Intent Classification and Slot Labeling in Goal-oriented Dialog Systems to Real-world Noise

Saab Mansour, Sailik Sengupta, Jason Krone•Tue Apr 13 2021

This work investigates how robust IC/SL models are to noisy data and designs aggregate data-augmentation approaches that increase the model performance across all seven noise types by +10.8\% for IC accuracy and +15 points for SL F1 on average.

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Maintaining Common Ground in Dynamic Environments

Takuma Udagawa, Akiko Aizawa•Fri May 28 2021

This work proposes a novel task setting to study the ability of both creating and maintaining common ground in dynamic environments, and collects a large-scale dataset of 5,617 dialogues to enable fine-grained evaluation and analysis of various dialogue systems.

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