End-to-end trainable dialogue system for natural task completion.
problem Challenges in creating task-oriented dialogue systems.
method Neural network-based, end-to-end trainable model with Wizard-of-Oz data collection.
result Model can converse naturally and complete tasks in a restaurant search domain.
Improves neural dialogue systems with snapshot learning and model architectures.
problem Improving interpretability and performance in neural dialogue systems.
method Various model architectures and snapshot learning for conditional generation.
result Snapshot learning leads to consistent performance improvements.
Neural User Simulator outperforms traditional ABUS in training dialogue systems.
problem Limited diversity and lack of natural language in ABUS.
method NUS learns user behavior from a corpus and generates natural language.
result NUS trained policies outperform ABUS in real user evaluations.
Improved dialogue system Q-learning with efficient exploration.
problem Efficient exploration in deep reinforcement learning for dialogue systems.
method Thompson sampling with Bayes-by-Backprop neural network, replay buffer spiking.
result Significantly faster learning compared to common strategies.
This paper collects a large dataset for conversational recommendation research.
problem Creating dialogue systems for conversational recommendation.
method Collects a large dataset (ReDial) and explores neural architectures, mechanisms, and methods for conversational recommendation systems.
result Demonstrates the utility of the collected dataset for systematic probing of model sub-components in conversational recommendation.
This paper examines methods to estimate uncertainty in neural networks for dialogue policy optimization.
problem Efficient exploration in neural network-based dialogue policy optimization.
method Extensive benchmark of deep Bayesian methods to extract uncertainty estimates from DQN.
result Combining uncertainty estimation methods with DQN improves sample efficiency and user experience.
Study aims to develop a humanoid robot dialogue system.
problem Current dialogue systems lack attention to non-verbal cues.
method Participated in a competition to develop a system with facial expressions and gaze control.
result Developed a humanoid robot dialogue system.
Two large medical dialogue datasets for improving healthcare.
problem Improving healthcare through better dialogue systems.
method Building two large-scale medical dialogue datasets: MedDialog-EN and MedDialog-CN.
result The datasets are the largest medical dialogue datasets to date.
A new psychotherapy dialogue model learns compressed text representations.
problem Modeling psychotherapy dialogues with small datasets.
method Nonparametric kernel functions and hashcode representations.
result Significantly outperforms state-of-the-art models in psychotherapy sessions.
End-to-end dialogue model learns from joint embeddings and user intent.
problem Challenges in reasoning and incorporating state-full knowledge in goal-oriented dialogues.
method Proposes an RNN-based end-to-end encoder-decoder architecture trained with joint embeddings and multi-task learning.
result Improves task-oriented dialogue system performance as shown by BLEU score evaluation.
Survey of corpora for data-driven dialogue systems.
problem Lack of publicly available datasets for data-driven dialogue systems.
method Wide survey of existing datasets, discussion of their characteristics and potential uses.
result Data-driven approaches are feasible and promising for dialogue systems.
Enhances dialogue model with persona attributes using adversarial learning.
problem Improving dialogue models to better capture speaker identity and topic.
method Adversarial learning framework with a dual discriminator system.
result phredGAN outperforms persona Seq2Seq model in various datasets.
Self-attentional models improve chatbot efficiency and performance.
problem Training efficient task-oriented dialogue generation systems.
method Applied self-attentional models to three datasets for chatbot training.
result Self-attentional models outperform recurrence-based models in efficiency and performance.
Optimizes dialogue success and length using multi-objective reinforcement learning.
problem Balancing multiple reward components in spoken dialogue systems.
method Structured multi-objective reinforcement learning to find optimal reward weights.
result Optimized reward weights significantly improve dialogue performance across six domains.
GOLOMB improves dialogue state tracking for unseen services.
problem Improving dialogue state tracking for multiple services and APIs.
method GOLOMB uses a BERT-based model that queries dialogue history with slot descriptions and values.
result GOLOMB achieves a joint goal accuracy of 53.97% on the SGD dataset.
Modeling long-range context for multi-function utterances in dialogues.
problem Complex dependencies across dialogue turns in long utterances.
method Adapted Convolutional Recurrent Neural Network (CRNN) to model interactions between utterances.
result Significantly outperforms existing work on CDA recognition on a tech forum dataset.
Improved dialogue system training with deep reinforcement learning.
problem Training efficient dialogue systems with large action spaces.
method Deep reinforcement learning, actor-critic methods, off-policy learning, ACER algorithm.
result ACER algorithm trains significantly faster in large action spaces.
LIDM uses latent variables to learn dialogue intentions, outperforming existing models.
problem Learning natural conversational variability in dialogue models.
method Neural variational inference with a discrete latent variable.
result LIDM outperforms existing models in goal-oriented dialogue scenarios.
The paper improves dialogue quality estimation using a novel user satisfaction model.
problem Improving dialogue quality estimation in spoken dialogue systems.
method Proposes a novel user satisfaction estimator based on BiLSTMs and reinforcement learning.
result The novel user satisfaction estimator outperforms previous models in terms of user satisfaction and task success.
Memory-Augmented Recurrent Networks improve dialogue coherence by expanding conversation history storage.
problem Fixed-size vectors limit dialogue coherence; attention mechanisms are computationally expensive.
method Introduce Neural Turing Machines (NTMs) to provide flexible and permanent storage for dialogue history.
result Improved perplexity performance compared to existing baselines.
Paper proposes a method to generate task-oriented dialogue queries.
problem Scarcity of training data for task-oriented dialogue systems.
method Conditional variational autoencoder for intent-specific sentence generation and query transfer protocol.
result Our method improves the diversity of generated queries without compromising quality.
Paper improves emotion expression in AI chatbots.
problem AI chatbots generate responses that lack emotion.
method Developed neural models to express specific emotions in generated responses.
result An encoder-decoder model with multiple attention layers performs best in expressing required emotion.
Reinforcement Learning improves personalized dialogue systems.
problem Personalized dialogue systems that generalize across users.
method Two RL-based approaches: single learner with user context features, and learner per context segmentation.
result RL methods outperform handcrafted systems in financial product recommendation.
Deep learning improves conversational recommender systems.
problem Creating effective goal-oriented dialogue systems through natural language interactions.
method Apply deep learning techniques to conversational recommender systems.
result Deep learning models enhance user preference learning in CRS.
FastSGT improves accuracy in BERT-based DST for SGD datasets.
problem Dialog State Tracking in goal-oriented dialogue systems.
method BERT-based model with two carry-over procedures and multi-head attention.
result Significantly improved accuracy compared to baseline model.
DGDS uses documents to center conversations, promising broader AI understanding.
problem DS classification by function is insufficient for complex conversations.
method Classify DS based on document grounding, analyzing classification, architecture, datasets, and models.
result DGDS can better represent current DS development trends and future AI understanding.
This paper guides practical solutions for neural text generation issues.
problem Undesired behavior in neural text generation models.
method Tuning end-to-end neural network models with encoder and decoder components.
result Resolving issues like truncated, repetitive, bland, and ungrammatical outputs.
Improves dialogue state tracking across multiple domains.
problem Incomplete domain ontology limits DST models' adaptability.
method Model DST as Q&A, using evolving knowledge graph.
result 5.80% and 12.21% relative improvement on datasets.
DLGNet improves dialogue response generation by leveraging transformer architecture.
problem Lack of relevance, diversity, and coherence in dialogue responses.
method Transformer-based model for dialogue response generation, incorporating long-range structures and random paddings.
result Significant improvements over state-of-the-art models on multiple datasets.
Neural Assistant integrates knowledge reasoning and dialogue generation in a single model.
problem Challenges in task-oriented dialog systems, including multi-turn language understanding and generation, knowledge retrieval and reasoning, and action prediction.
method Develops a single neural network model that jointly predicts text responses and actions from conversation history and external knowledge.
result The model learns to reason on external knowledge with weak supervision, improving factual accuracy and language generation performance.
ConvMFiT classifies client utterances in counseling dialogues.
problem Classifying client utterances for better counseling outcomes.
method Fine-tuning a pre-trained conversation model on counselor-client dialogues.
result ConvMFiT outperforms state-of-the-art models in classification.
Enhances persona-based conversation model for multi-turn dialogue.
problem Improving persona-based conversation models for multi-turn dialogue.
method Introduced additional input modality into hredGAN to capture external attributes.
result Persona hredGAN (phredGAN) outperforms existing models in multi-turn dialogue corpora. Pretraining method enhances dialogue representation learning across various tasks.
problem Scarce labeled data for specific dialogue tasks.
method Multi-task unsupervised pretraining with natural training objectives.
result Significant improvement in downstream tasks without encoder discrimination.
Multiresolution RNN improves dialogue response generation.
problem Generating relevant and on-topic responses in dialogue systems.
method Introducing a multiresolution recurrent neural network that models natural language generation as two parallel sequences.
result The model outperforms competing approaches in dialogue response generation on the Ubuntu domain and appears more relevant on Twitter.
Improves dialogue response model interpretability using attention and regularization.
problem Improving interpretability of dual encoder models for dialogue response suggestions.
method Integrates attention mechanism and novel regularization loss to emphasize important words.
result Improves model accuracy and interpretability compared to existing methods.
DAG-LSTM improves DA classification in group chats.
problem DA classification in multi-party conversations.
method Directed-Acyclic-Graph LSTM (DAG-LSTM) exploiting turn-taking structure.
result DAG-LSTM outperforms existing methods by 0.8% in accuracy and 1.2% in macro-F1 score.
SuTaT creates dialogue summaries for tete-a-tetes without labeled data.
problem Lack of high-quality paired dialogue-summary data.
method Unsupervised model for tete-a-tetes, modeling customer and agent roles separately.
result SuTaT outperforms on automatic and human evaluations.
Study proposes a multi-agent framework to mitigate bias in sentiment analysis.
problem Bias in sentiment analysis models.
method Integrates multiple LLMs, incorporates dialogue sessions, and uses probabilistic prediction.
result KCS+IBC reduces entropy and increases variance, suggesting improved balance between aggregation and diversity.
This paper proposes a benchmarking environment for RL-based dialogue management.
problem Lack of a common benchmarking framework for RL models in dialogue management.
method Developed a set of simulated environments and compared RL models (DQN, A2C, Natural Actor-Critic, GP-SARSA).
result Demonstrated the effectiveness of various RL models in dialogue management.
Self-feeding chatbots learn from user feedback to improve performance.
problem Lack of training data after deployment of dialogue agents.
method Extracts new training examples from user responses and uses feedback to improve dialogue abilities.
result Self-feeding chatbots significantly improve performance on PersonaChat dataset.
Improved model-based reinforcement learning for interactive dialogue tasks reduces sample needs and improves performance.
problem Limited data and high sample cost in interactive dialogue systems.
method Model-based actor-critic approach with an environment model and planner.
result 70 times fewer samples required compared to baseline model-free algorithm, with 2x better asymptotic performance.
Improves slot key and value prediction for unseen entities.
problem Dealing with unseen slot keys and values in real-world dialogue systems.
method Leverages external knowledge bases to project slots into an attribute space and generate candidate keys and values.
result Significant improvements in F1 score and accuracy (57.7% and 82.7%, respectively) over a previous approach.
Adversarial learning improves multi-turn dialogue response generation.
problem Generating coherent and diverse multi-turn dialogue responses.
method hredGAN based on conditional GANs with HRED generator and word-level RNN discriminator.
result hredGAN generates more informative and diverse responses with limited data.
Study combines chit-chat and goal-oriented dialogue in fantasy games.
problem Combining naturalistic chit-chat with goal-oriented tasks in fantasy games.
method Trained a goal-oriented model with reinforcement learning against an imitation-learned chit-chat model using two approaches.
result Both models outperform a baseline and can converse naturally to achieve goals.
Automated dialogue quality evaluation using user satisfaction estimates across multiple domains.
problem Lack of automated and domain-independent dialogue quality evaluation metrics.
method Created a new Response Quality annotation scheme, introduced five domain-independent feature sets, and experimented with six machine learning models.
result Gradient Boosting Regression model achieved best prediction performance, with a 16% relative improvement in binary satisfaction class prediction accuracy.
Paper uses ML to predict utility in APS dialogue outcomes.
problem Predict utility for different user subpopulations in APS.
method Develops EAI and EDS ML methods to predict utility functions.
result EDS more effective at predicting utility functions.
Improved RL for knowledge graph reasoning with entity types.
problem Challenges in path-based relational reasoning over knowledge graphs.
method Type-enhanced RL agent using GNN for neighborhood information.
result Outperforms state-of-the-art RL methods and discovers novel paths.
New method improves dialogue agents focusing on simple utterances.
problem Dialogue agents often focus on simple utterances and suboptimal policies.
method Tempered Policy Gradient (TPG) methods to improve dialogue performance.
result Significant improvements in dialogue performance, especially in producing convincing utterances.