27,872 research outputs found
Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation
This paper surveys the current state of the art in Natural Language
Generation (NLG), defined as the task of generating text or speech from
non-linguistic input. A survey of NLG is timely in view of the changes that the
field has undergone over the past decade or so, especially in relation to new
(usually data-driven) methods, as well as new applications of NLG technology.
This survey therefore aims to (a) give an up-to-date synthesis of research on
the core tasks in NLG and the architectures adopted in which such tasks are
organised; (b) highlight a number of relatively recent research topics that
have arisen partly as a result of growing synergies between NLG and other areas
of artificial intelligence; (c) draw attention to the challenges in NLG
evaluation, relating them to similar challenges faced in other areas of Natural
Language Processing, with an emphasis on different evaluation methods and the
relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118
pages, 8 figures, 1 tabl
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Data-Driven Policy Optimisation for Multi-Domain Task-Oriented Dialogue
Recent developments in machine learning along with a general shift in the public attitude towards digital personal assistants has opened new frontiers for conversational systems. Nevertheless, building data-driven multi-domain conversational agents that act optimally given a dialogue context is an open challenge. The first step towards that goal is developing an efficient way of learning a dialogue policy in new domains. Secondly, it is important to have the ability to collect and utilise human-human conversational data to bootstrap an agent's knowledge. The work presented in this thesis demonstrates how a neural dialogue manager fine-tuned with reinforcement learning presents a viable approach for learning a dialogue policy efficiently and across many domains.
The thesis starts by introducing a dialogue management module that learns through interactions to act optimally given a current context of a conversation. The current shift towards neural, parameter-rich systems does not fully address the problem of error noise coming from speech recognition or natural language understanding components. A Bayesian approach is therefore proposed to learn more robust and effective policy management in direct interactions without any prior data. By putting a distribution over model weights, the learning agent is less prone to overfit to particular dialogue realizations and a more efficient exploration policy can be therefore employed. The results show that deep reinforcement learning performs on par with non-parametric models even in a low data regime while significantly reducing the computational complexity compared with the previous state-of-the-art.
The deployment of a dialogue manager without any pre-training on human conversations is not a viable option from an industry perspective. However, the progress in building statistical systems, particularly dialogue managers, is hindered by the scale of data available. To address this fundamental obstacle, a novel data-collection pipeline entirely based on crowdsourcing without the need for hiring professional annotators is introduced. The validation of the approach results in the collection of the Multi-Domain Wizard-of-Oz dataset (MultiWOZ), a fully labeled collection of human-human written conversations spanning over multiple domains and topics. The proposed dataset creates a set of new benchmarks (belief tracking, policy optimisation, and response generation) significantly raising the complexity of analysed dialogues.
The collected dataset serves as a foundation for a novel reinforcement learning (RL)-based approach for training a multi-domain dialogue manager. A Multi-Action and Slot Dialogue Agent (MASDA) is proposed to combat some limitations: 1) handling complex multi-domain dialogues with multiple concurrent actions present in a single turn; and 2) lack of interpretability, which consequently impedes the use of intermediate signals (e.g., dialogue turn annotations) if such signals are available. MASDA explicitly models system acts and slots using intermediate signals, resulting in an improved task-based end-to-end framework. The model can also select concurrent actions in a single turn, thus enriching the representation of the generated responses. The proposed framework allows for RL training of dialogue task completion metrics when dealing with concurrent actions. The results demonstrate the advantages of both 1) handling concurrent actions and 2) exploiting intermediate signals: MASDA outperforms previous end-to-end frameworks while also offering improved scalability.EPSR
A Survey of Multi-task Learning in Natural Language Processing: Regarding Task Relatedness and Training Methods
Multi-task learning (MTL) has become increasingly popular in natural language
processing (NLP) because it improves the performance of related tasks by
exploiting their commonalities and differences. Nevertheless, it is still not
understood very well how multi-task learning can be implemented based on the
relatedness of training tasks. In this survey, we review recent advances of
multi-task learning methods in NLP, with the aim of summarizing them into two
general multi-task training methods based on their task relatedness: (i) joint
training and (ii) multi-step training. We present examples in various NLP
downstream applications, summarize the task relationships and discuss future
directions of this promising topic.Comment: Accepted to EACL 2023 as regular long pape
Towards a Neural Era in Dialogue Management for Collaboration: A Literature Survey
Dialogue-based human-AI collaboration can revolutionize collaborative
problem-solving, creative exploration, and social support. To realize this
goal, the development of automated agents proficient in skills such as
negotiating, following instructions, establishing common ground, and
progressing shared tasks is essential. This survey begins by reviewing the
evolution of dialogue management paradigms in collaborative dialogue systems,
from traditional handcrafted and information-state based methods to AI
planning-inspired approaches. It then shifts focus to contemporary data-driven
dialogue management techniques, which seek to transfer deep learning successes
from form-filling and open-domain settings to collaborative contexts. The paper
proceeds to analyze a selected set of recent works that apply neural approaches
to collaborative dialogue management, spotlighting prevailing trends in the
field. This survey hopes to provide foundational background for future
advancements in collaborative dialogue management, particularly as the dialogue
systems community continues to embrace the potential of large language models
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