Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Deep learning methods employ multiple processing layers to learn hierarchical representations of data, and have produced state-of-the-art results in many domains. Recently, a variety of model designs and methods have blossomed in the context of natural language processing (NLP). In this paper, we review significant deep learning related models and methods that have been employed for numerous NLP tasks and provide a walk-through of their evolution. We also summarize, compare and contrast the various models and put forward a detailed understanding of the past, present and future of deep learning in NLP.

Recent Trends in Deep Learning Based Natural Language Processing [Review Article]

As a new way of training generative models, Generative Adversarial
Net (GAN) that uses a discriminative model to guide
the training of the generative model has enjoyed considerable
success in generating real-valued data. However, it has limitations
when the goal is for generating sequences of discrete
tokens. A major reason lies in that the discrete outputs from
the generative model make it difficult to pass the gradient update
from the discriminative model to the generative model.
Also, the discriminative model can only assess a complete
sequence, while for a partially generated sequence, it is nontrivial
to balance its current score and the future one once
the entire sequence has been generated. In this paper, we propose
a sequence generation framework, called SeqGAN, to
solve the problems. Modeling the data generator as a stochastic
policy in reinforcement learning (RL), SeqGAN bypasses
the generator differentiation problem by directly performing
gradient policy update. The RL reward signal comes from
the GAN discriminator judged on a complete sequence, and
is passed back to the intermediate state-action steps using
Monte Carlo search. Extensive experiments on synthetic data
and real-world tasks demonstrate significant improvements
over strong baselines.

/pdf/seqgan-sequence-generative-adversarial-nets-with-policy-3v3kdvty73.pdf

Seqgan: sequence generative adversarial nets with policy gradient

Sequence-to-sequence neural network models for generation of conversational responses tend to generate safe, commonplace responses (e.g., I don’t know) regardless of the input. We suggest that the traditional objective function, i.e., the likelihood of output (response) given input (message) is unsuited to response generation tasks. Instead we propose using Maximum Mutual Information (MMI) as the objective function in neural models. Experimental results demonstrate that the proposed MMI models produce more diverse, interesting, and appropriate responses, yielding substantive gains in BLEU scores on two conversational datasets and in human evaluations.

/pdf/a-diversity-promoting-objective-function-for-neural-jymgikudii.pdf

A Diversity-Promoting Objective Function for Neural Conversation Models

We introduce the use of rectified linear units (ReLU) as the classification function in a deep neural network (DNN). Conventionally, ReLU is used as an activation function in DNNs, with Softmax function as their classification function. However, there have been several studies on using a classification function other than Softmax, and this study is an addition to those. We accomplish this by taking the activation of the penultimate layer $h_{n - 1}$ in a neural network, then multiply it by weight parameters $\theta$ to get the raw scores $o_{i}$. Afterwards, we threshold the raw scores $o_{i}$ by $0$, i.e. $f(o) = \max(0, o_{i})$, where $f(o)$ is the ReLU function. We provide class predictions $\hat{y}$ through argmax function, i.e. argmax $f(x)$.

Deep Learning using Rectified Linear Units (ReLU)

Statistical dialog systems (SDSs) are motivated by the need for a data-driven framework that reduces the cost of laboriously handcrafting complex dialog managers and that provides robustness against the errors created by speech recognizers operating in noisy environments. By including an explicit Bayesian model of uncertainty and by optimizing the policy via a reward-driven process, partially observable Markov decision processes (POMDPs) provide such a framework. However, exact model representation and optimization is computationally intractable. Hence, the practical application of POMDP-based systems requires efficient algorithms and carefully constructed approximations. This review article provides an overview of the current state of the art in the development of POMDP-based spoken dialog systems.

/pdf/pomdp-based-statistical-spoken-dialog-systems-a-review-52td9wfczr.pdf

POMDP-Based Statistical Spoken Dialog Systems: A Review

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 problem for each component. In this work we introduce a neural network-based text-in, text-out end-to-end trainable goal-oriented dialogue system along with a new way of collecting dialogue data based on a novel pipe-lined Wizard-of-Oz framework. This approach allows us to develop dialogue systems easily and without making too many assumptions about the task at hand. The results show that the model can converse with human subjects naturally whilst helping them to accomplish tasks in a restaurant search domain.

/pdf/a-network-based-end-to-end-trainable-task-oriented-dialogue-2psoou4hao.pdf

A Network-based End-to-End Trainable Task-oriented Dialogue System

Natural language generation (NLG) is a critical component of spoken dialogue and it has a significant impact both on usability and perceived quality. Most NLG systems in common use employ rules and heuristics and tend to generate rigid and stylised responses without the natural variation of human language. They are also not easily scaled to systems covering multiple domains and languages. This paper presents a statistical language generator based on a semantically controlled Long Short-term Memory (LSTM) structure. The LSTM generator can learn from unaligned data by jointly optimising sentence planning and surface realisation using a simple cross entropy training criterion, and language variation can be easily achieved by sampling from output candidates. With fewer heuristics, an objective evaluation in two differing test domains showed the proposed method improved performance compared to previous methods. Human judges scored the LSTM system higher on informativeness and naturalness and overall preferred it to the other systems..

/pdf/semantically-conditioned-lstm-based-natural-language-3nrkes6te0.pdf

Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems

Even though machine learning has become the major scene in dialogue research community, the real breakthrough has been blocked by the scale of data available. To address this fundamental obstacle, we introduce the Multi-Domain Wizard-of-Oz dataset (MultiWOZ), a fully-labeled collection of human-human written conversations spanning over multiple domains and topics. At a size of $10$k dialogues, it is at least one order of magnitude larger than all previous annotated task-oriented corpora. The contribution of this work apart from the open-sourced dataset labelled with dialogue belief states and dialogue actions is two-fold: firstly, a detailed description of the data collection procedure along with a summary of data structure and analysis is provided. The proposed data-collection pipeline is entirely based on crowd-sourcing without the need of hiring professional annotators; secondly, a set of benchmark results of belief tracking, dialogue act and response generation is reported, which shows the usability of the data and sets a baseline for future studies.

MultiWOZ - A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling

http://mi.eng.cam.ac.uk/~sjy/papers/ygkm10.pdf

Milica Gasic

Papers

POMDP-Based Statistical Spoken Dialog Systems: A Review

A Network-based End-to-End Trainable Task-oriented Dialogue System

Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems

MultiWOZ - A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling

The Hidden Information State model: A practical framework for POMDP-based spoken dialogue management