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.
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.
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.
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.
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.
Automatic continuous speech recognition (CSR) is sufficiently mature that a variety of real world applications are now possible including large vocabulary transcription and interactive spoken dialogues. This paper reviews the evolution of the statistical modelling techniques which underlie current-day systems, specific…
Boosting is a general method of generating many simple classification rules and combining them into a single, highly accurate rule. In this talk, I will review the AdaBoost boosting algorithm and some of its underlying theory, and then look at how this theory has helped us to face some of the challenges of applying Ada…
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.
System diagnoses Alzheimer's disease from spoken language using multi-modal features.
problem Early diagnosis of Alzheimer's disease from spoken language.
method Classification system based on spoken language using three approaches (N-gram, i-vector, x-vector).
result Accuracy of 83.6% on the cookie picture description task from Pitt Corpus dementia bank.
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.
Teaching machines to accomplish tasks by conversing naturally with humans is challenging. Currently, developing task-oriented dialogue systems requires creating multiple components and typically this involves either a large amount of handcrafting, or acquiring costly labelled datasets to solve a statistical learning pr…
Expanding spoken language understanding to handle complex entities and intents.
problem Handling compound entities and intents in spoken language understanding.
method Introducing a domain-agnostic shallow parser that handles linguistic coordination, learning domain-independent and slot-independent features.
result The model learns to segment conjunct boundaries of various phrasal categories and improves generalization across different slot types using adversarial training.
NLP techniques improve drug discovery by analyzing chemical and protein text.
problem Improving drug discovery through better analysis of chemical and protein text.
method Natural language processing techniques applied to biochemical entities.
result Enhanced prediction of molecular properties and design of novel molecules.
During the past decade, several areas of speech and language understanding have witnessed substantial breakthroughs from the use of data-driven models. In the area of dialogue systems, the trend is less obvious, and most practical systems are still built through significant engineering and expert knowledge. Nevertheles…
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.
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.
Improved spoken English intelligibility with computer recognition and feature extraction.
problem Improving spoken English pronunciation and intelligibility.
method Automatic speech recognition using PocketSphinx alignment and feature extraction with SVM classifier probability prediction.
result SVM models achieve 82 percent agreement with human transcriptions, up from 75 percent.
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.
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.
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.
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 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.
Extends reinforcement learning to continuous state spaces with safety constraints.
problem Safety-critical reinforcement learning in continuous state spaces with unknown dynamics.
method Introduces a novel Budgeted Bellman Optimality operator and applies it to continuous state spaces.
result Validated on spoken dialogue and autonomous driving applications.
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.
Spoken language translation (SLT) has become very important in an increasingly globalized world. Machine translation (MT) for automatic speech recognition (ASR) systems is a major challenge of great interest. This research investigates that automatic sentence segmentation of speech that is important for enriching speec…
We present a new algorithm that significantly improves the efficiency of exploration for deep Q-learning agents in dialogue systems. Our agents explore via Thompson sampling, drawing Monte Carlo samples from a Bayes-by-Backprop neural network. Our algorithm learns much faster than common exploration strategies such as …
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.
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.
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.
End-to-end neural network improves QbE-STD in multilingual speech search.
problem Query by example spoken term detection in zero-resource scenarios.
method Use of multilingual bottleneck features and CNN for pattern matching, integrated into a fully neural network framework.
result CNN-based matching outperforms DTW-based matching using bottleneck features.
ACI converts call center conversations into actionable data.
problem Real-time spoken language understanding for call center conversations.
method Combines speech recognition, entity and intent recognition, and a business rules engine.
result ACI converts live audio into structured events for real-time supervision and assistance.
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.
New model learns coupled representations for domains, intents, and slots.
problem Representation learning for domains, intents, and slots in spoken language understanding.
method Proposes a model that learns coupled representations by aggregating slot and intent representations based on their hierarchical relationships.
result Improved performance on contextual cross-domain reranking task.
Enhances spoken speech quality using EEG signals.
problem Improves speech clarity in noisy environments.
method Generative adversarial network (GAN), gated recurrent unit (GRU), temporal convolutional network (TCN) regression models.
result Significant improvement in speech enhancement quality compared to traditional methods.
Developing a dialogue agent that is capable of making autonomous decisions and communicating by natural language is one of the long-term goals of machine learning research. Traditional approaches either rely on hand-crafting a small state-action set for applying reinforcement learning that is not scalable or constructi…
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.
Improves E2E ASR performance on numeric sequences with additional training data and denormalization.
problem Challenges in recognizing numeric sequences out-of-vocabulary in ASR systems.
method Uses text-to-speech for additional numeric training data and a small-footprint neural network for denormalization.
result Reduction of WER by up to a factor of 8 in the longest numeric sequences.
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.
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.
Recently a variety of LSTM-based conditional language models (LM) have been applied across a range of language generation tasks. In this work we study various model architectures and different ways to represent and aggregate the source information in an end-to-end neural dialogue system framework. A method called snaps…
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.
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.
This research explores the effects of various training settings on a Polish to English Statistical Machine Translation system for spoken language. Various elements of the TED, Europarl, and OPUS parallel text corpora were used as the basis for training of language models, for development, tuning and testing of the tran…