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Found 36 papers, 5 papers with code
Unsupervised Inflection Generation Using Neural Language Modeling
The use of Deep Neural Network architectures for Language Modeling has recently seen a tremendous increase in interest in the field of NLP with the advent of transfer learning and the shift in focus from rule-based and predictive models (supervised learning) to generative or unsupervised models to solve the long-standing problems in NLP like Information Extraction or Question Answering.
Addressing Objects and Their Relations: The Conversational Entity Dialogue Model
Statistical spoken dialogue systems usually rely on a single- or multi-domain dialogue model that is restricted in its capabilities of modelling complex dialogue structures, e. g., relations.
Nearly Zero-Shot Learning for Semantic Decoding in Spoken Dialogue Systems
This paper presents two ways of dealing with scarce data in semantic decoding using N-Best speech recognition hypotheses.
A Benchmarking Environment for Reinforcement Learning Based Task Oriented Dialogue Management
Dialogue assistants are rapidly becoming an indispensable daily aid.
DialPort, Gone Live: An Update After A Year of Development
DialPort collects user data for connected spoken dialog systems.
Reward-Balancing for Statistical Spoken Dialogue Systems using Multi-objective Reinforcement Learning
Reinforcement learning is widely used for dialogue policy optimization where the reward function often consists of more than one component, e. g., the dialogue success and the dialogue length.
Multi-Objective Reinforcement Learning
reinforcement-learning
+1
Sample-efficient Actor-Critic Reinforcement Learning with Supervised Data for Dialogue Management
Firstly, to speed up the learning process, two sample-efficient neural networks algorithms: trust region actor-critic with experience replay (TRACER) and episodic natural actor-critic with experience replay (eNACER) are presented.
Semantic Specialisation of Distributional Word Vector Spaces using Monolingual and Cross-Lingual Constraints
We present Attract-Repel, an algorithm for improving the semantic quality of word vectors by injecting constraints extracted from lexical resources.
Morph-fitting: Fine-Tuning Word Vector Spaces with Simple Language-Specific Rules
Morphologically rich languages accentuate two properties of distributional vector space models: 1) the difficulty of inducing accurate representations for low-frequency word forms; and 2) insensitivity to distinct lexical relations that have similar distributional signatures.
Latent Intention Dialogue Models
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.
Semantic Specialization of Distributional Word Vector Spaces using Monolingual and Cross-Lingual Constraints
We present Attract-Repel, an algorithm for improving the semantic quality of word vectors by injecting constraints extracted from lexical resources.
Exploiting Sentence and Context Representations in Deep Neural Models for Spoken Language Understanding
This paper presents a deep learning architecture for the semantic decoder component of a Statistical Spoken Dialogue System.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+6
Dialogue manager domain adaptation using Gaussian process reinforcement learning
Spoken dialogue systems allow humans to interact with machines using natural speech.
Neural Belief Tracker: Data-Driven Dialogue State Tracking
One of the core components of modern spoken dialogue systems is the belief tracker, which estimates the user's goal at every step of the dialogue.
Conditional Generation and Snapshot Learning in Neural Dialogue Systems
Recently a variety of LSTM-based conditional language models (LM) have been applied across a range of language generation tasks.
Continuously Learning Neural Dialogue Management
We describe a two-step approach for dialogue management in task-oriented spoken dialogue systems.
On-line Active Reward Learning for Policy Optimisation in Spoken Dialogue Systems
The ability to compute an accurate reward function is essential for optimising a dialogue policy via reinforcement learning.
Learning Tone and Attribution for Financial Text Mining
Previous studies have only applied manual content analysis on a small scale to reveal such a bias in the narrative section of annual financial reports.
A Network-based End-to-End Trainable Task-oriented Dialogue System
Teaching machines to accomplish tasks by conversing naturally with humans is challenging.
Multi-domain Neural Network Language Generation for Spoken Dialogue Systems
Moving from limited-domain natural language generation (NLG) to open domain is difficult because the number of semantic input combinations grows exponentially with the number of domains.
Counter-fitting Word Vectors to Linguistic Constraints
In this work, we present a novel counter-fitting method which injects antonymy and synonymy constraints into vector space representations in order to improve the vectors' capability for judging semantic similarity.
Reward Shaping with Recurrent Neural Networks for Speeding up On-Line Policy Learning in Spoken Dialogue Systems
Reward shaping is one promising technique for addressing these concerns.
Learning from Real Users: Rating Dialogue Success with Neural Networks for Reinforcement Learning in Spoken Dialogue Systems
The models are trained on dialogues generated by a simulated user and the best model is then used to train a policy on-line which is shown to perform at least as well as a baseline system using prior knowledge of the user's task.
Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems
Natural language generation (NLG) is a critical component of spoken dialogue and it has a significant impact both on usability and perceived quality.
Stochastic Language Generation in Dialogue using Recurrent Neural Networks with Convolutional Sentence Reranking
The natural language generation (NLG) component of a spoken dialogue system (SDS) usually needs a substantial amount of handcrafting or a well-labeled dataset to be trained on.
Multi-domain Dialog State Tracking using Recurrent Neural Networks
Dialog state tracking is a key component of many modern dialog systems, most of which are designed with a single, well-defined domain in mind.
Detecting Document Structure in a Very Large Corpus of UK Financial Reports
In this paper we present the evaluation of our automatic methods for detecting and extracting document structure in annual financial reports.
