46,012 research outputs found
Neural Approaches to Conversational AI
The present paper surveys neural approaches to conversational AI that have
been developed in the last few years. We group conversational systems into
three categories: (1) question answering agents, (2) task-oriented dialogue
agents, and (3) chatbots. For each category, we present a review of
state-of-the-art neural approaches, draw the connection between them and
traditional approaches, and discuss the progress that has been made and
challenges still being faced, using specific systems and models as case
studies.Comment: Foundations and Trends in Information Retrieval (95 pages
A Survey on Dialogue Systems: Recent Advances and New Frontiers
Dialogue systems have attracted more and more attention. Recent advances on
dialogue systems are overwhelmingly contributed by deep learning techniques,
which have been employed to enhance a wide range of big data applications such
as computer vision, natural language processing, and recommender systems. For
dialogue systems, deep learning can leverage a massive amount of data to learn
meaningful feature representations and response generation strategies, while
requiring a minimum amount of hand-crafting. In this article, we give an
overview to these recent advances on dialogue systems from various perspectives
and discuss some possible research directions. In particular, we generally
divide existing dialogue systems into task-oriented and non-task-oriented
models, then detail how deep learning techniques help them with representative
algorithms and finally discuss some appealing research directions that can
bring the dialogue system research into a new frontier.Comment: 13 pages. arXiv admin note: text overlap with arXiv:1703.01008 by
other author
Production Ready Chatbots: Generate if not Retrieve
In this paper, we present a hybrid model that combines a neural
conversational model and a rule-based graph dialogue system that assists users
in scheduling reminders through a chat conversation. The graph based system has
high precision and provides a grammatically accurate response but has a low
recall. The neural conversation model can cater to a variety of requests, as it
generates the responses word by word as opposed to using canned responses. The
hybrid system shows significant improvements over the existing baseline system
of rule based approach and caters to complex queries with a domain-restricted
neural model. Restricting the conversation topic and combination of graph based
retrieval system with a neural generative model makes the final system robust
enough for a real world application.Comment: DEEPDIAL-18, AAAI-201
Combining Search with Structured Data to Create a More Engaging User Experience in Open Domain Dialogue
The greatest challenges in building sophisticated open-domain conversational
agents arise directly from the potential for ongoing mixed-initiative
multi-turn dialogues, which do not follow a particular plan or pursue a
particular fixed information need. In order to make coherent conversational
contributions in this context, a conversational agent must be able to track the
types and attributes of the entities under discussion in the conversation and
know how they are related. In some cases, the agent can rely on structured
information sources to help identify the relevant semantic relations and
produce a turn, but in other cases, the only content available comes from
search, and it may be unclear which semantic relations hold between the search
results and the discourse context. A further constraint is that the system must
produce its contribution to the ongoing conversation in real-time. This paper
describes our experience building SlugBot for the 2017 Alexa Prize, and
discusses how we leveraged search and structured data from different sources to
help SlugBot produce dialogic turns and carry on conversations whose length
over the semi-finals user evaluation period averaged 8:17 minutes.Comment: SCAI 201
Iterative Policy Learning in End-to-End Trainable Task-Oriented Neural Dialog Models
In this paper, we present a deep reinforcement learning (RL) framework for
iterative dialog policy optimization in end-to-end task-oriented dialog
systems. Popular approaches in learning dialog policy with RL include letting a
dialog agent to learn against a user simulator. Building a reliable user
simulator, however, is not trivial, often as difficult as building a good
dialog agent. We address this challenge by jointly optimizing the dialog agent
and the user simulator with deep RL by simulating dialogs between the two
agents. We first bootstrap a basic dialog agent and a basic user simulator by
learning directly from dialog corpora with supervised training. We then improve
them further by letting the two agents to conduct task-oriented dialogs and
iteratively optimizing their policies with deep RL. Both the dialog agent and
the user simulator are designed with neural network models that can be trained
end-to-end. Our experiment results show that the proposed method leads to
promising improvements on task success rate and total task reward comparing to
supervised training and single-agent RL training baseline models.Comment: Accepted at ASRU 201
Context-Sensitive Generation Network for Handing Unknown Slot Values in Dialogue State Tracking
As a key component in a dialogue system, dialogue state tracking plays an
important role. It is very important for dialogue state tracking to deal with
the problem of unknown slot values. As far as we known, almost all existing
approaches depend on pointer network to solve the unknown slot value problem.
These pointer network-based methods usually have a hidden assumption that there
is at most one out-of-vocabulary word in an unknown slot value because of the
character of a pointer network. However, often, there are multiple
out-of-vocabulary words in an unknown slot value, and it makes the existing
methods perform bad. To tackle the problem, in this paper, we propose a novel
Context-Sensitive Generation network (CSG) which can facilitate the
representation of out-of-vocabulary words when generating the unknown slot
value. Extensive experiments show that our proposed method performs better than
the state-of-the-art baselines
End-to-End Optimization of Task-Oriented Dialogue Model with Deep Reinforcement Learning
In this paper, we present a neural network based task-oriented dialogue
system that can be optimized end-to-end with deep reinforcement learning (RL).
The system is able to track dialogue state, interface with knowledge bases, and
incorporate query results into agent's responses to successfully complete
task-oriented dialogues. Dialogue policy learning is conducted with a hybrid
supervised and deep RL methods. We first train the dialogue agent in a
supervised manner by learning directly from task-oriented dialogue corpora, and
further optimize it with deep RL during its interaction with users. In the
experiments on two different dialogue task domains, our model demonstrates
robust performance in tracking dialogue state and producing reasonable system
responses. We show that deep RL based optimization leads to significant
improvement on task success rate and reduction in dialogue length comparing to
supervised training model. We further show benefits of training task-oriented
dialogue model end-to-end comparing to component-wise optimization with
experiment results on dialogue simulations and human evaluations
The role of robust semantic analysis in spoken language dialogue systems
In this paper we summarized a framework for designing grammar-based procedure
for the automatic extraction of the semantic content from spoken queries.
Starting with a case study and following an approach which combines the notions
of fuzziness and robustness in sentence parsing, we showed we built practical
domain-dependent rules which can be applied whenever it is possible to
superimpose a sentence-level semantic structure to a text without relying on a
previous deep syntactical analysis. This kind of procedure can be also
profitably used as a pre-processing tool in order to cut out part of the
sentence which have been recognized to have no relevance in the understanding
process. In the case of particular dialogue applications where there is no need
to build a complex semantic structure (e.g. word spotting or excerpting) the
presented methodology may represent an efficient alternative solution to a
sequential composition of deep linguistic analysis modules. Even if the query
generation problem may not seem a critical application it should be held in
mind that the sentence processing must be done on-line. Having this kind of
constraints we cannot design our system without caring for efficiency and thus
provide an immediate response. Another critical issue is related to whole
robustness of the system. In our case study we tried to make experiences on how
it is possible to deal with an unreliable and noisy input without asking the
user for any repetition or clarification. This may correspond to a similar
problem one may have when processing text coming from informal writing such as
e-mails, news and in many cases Web pages where it is often the case to have
irrelevant surrounding information.Comment: 6 page
Few-Shot Generalization Across Dialogue Tasks
Machine-learning based dialogue managers are able to learn complex behaviors
in order to complete a task, but it is not straightforward to extend their
capabilities to new domains. We investigate different policies' ability to
handle uncooperative user behavior, and how well expertise in completing one
task (such as restaurant reservations) can be reapplied when learning a new one
(e.g. booking a hotel). We introduce the Recurrent Embedding Dialogue Policy
(REDP), which embeds system actions and dialogue states in the same vector
space. REDP contains a memory component and attention mechanism based on a
modified Neural Turing Machine, and significantly outperforms a baseline LSTM
classifier on this task. We also show that both our architecture and baseline
solve the bAbI dialogue task, achieving 100% test accuracy
Sample-efficient Actor-Critic Reinforcement Learning with Supervised Data for Dialogue Management
Deep reinforcement learning (RL) methods have significant potential for
dialogue policy optimisation. However, they suffer from a poor performance in
the early stages of learning. This is especially problematic for on-line
learning with real users. Two approaches are introduced to tackle this problem.
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.
For TRACER, the trust region helps to control the learning step size and avoid
catastrophic model changes. For eNACER, the natural gradient identifies the
steepest ascent direction in policy space to speed up the convergence. Both
models employ off-policy learning with experience replay to improve
sample-efficiency. Secondly, to mitigate the cold start issue, a corpus of
demonstration data is utilised to pre-train the models prior to on-line
reinforcement learning. Combining these two approaches, we demonstrate a
practical approach to learn deep RL-based dialogue policies and demonstrate
their effectiveness in a task-oriented information seeking domain.Comment: Accepted as a long paper in SigDial 201
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