1,404 research outputs found
Learning how to learn: an adaptive dialogue agent for incrementally learning visually grounded word meanings
We present an optimised multi-modal dialogue agent for interactive learning
of visually grounded word meanings from a human tutor, trained on real
human-human tutoring data. Within a life-long interactive learning period, the
agent, trained using Reinforcement Learning (RL), must be able to handle
natural conversations with human users and achieve good learning performance
(accuracy) while minimising human effort in the learning process. We train and
evaluate this system in interaction with a simulated human tutor, which is
built on the BURCHAK corpus -- a Human-Human Dialogue dataset for the visual
learning task. The results show that: 1) The learned policy can coherently
interact with the simulated user to achieve the goal of the task (i.e. learning
visual attributes of objects, e.g. colour and shape); and 2) it finds a better
trade-off between classifier accuracy and tutoring costs than hand-crafted
rule-based policies, including ones with dynamic policies.Comment: 10 pages, RoboNLP Workshop from ACL Conferenc
A Survey of the Evolution of Language Model-Based Dialogue Systems
Dialogue systems, including task-oriented_dialogue_system (TOD) and
open-domain_dialogue_system (ODD), have undergone significant transformations,
with language_models (LM) playing a central role. This survey delves into the
historical trajectory of dialogue systems, elucidating their intricate
relationship with advancements in language models by categorizing this
evolution into four distinct stages, each marked by pivotal LM breakthroughs:
1) Early_Stage: characterized by statistical LMs, resulting in rule-based or
machine-learning-driven dialogue_systems; 2) Independent development of TOD and
ODD based on neural_language_models (NLM; e.g., LSTM and GRU), since NLMs lack
intrinsic knowledge in their parameters; 3) fusion between different types of
dialogue systems with the advert of pre-trained_language_models (PLMs),
starting from the fusion between four_sub-tasks_within_TOD, and then
TOD_with_ODD; and 4) current LLM-based_dialogue_system, wherein LLMs can be
used to conduct TOD and ODD seamlessly. Thus, our survey provides a
chronological perspective aligned with LM breakthroughs, offering a
comprehensive review of state-of-the-art research outcomes. What's more, we
focus on emerging topics and discuss open challenges, providing valuable
insights into future directions for LLM-based_dialogue_systems. Through this
exploration, we pave the way for a deeper_comprehension of the evolution,
guiding future developments in LM-based dialogue_systems
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Continually improving grounded natural language understanding through human-robot dialog
As robots become ubiquitous in homes and workplaces such as hospitals and factories, they must be able to communicate with humans. Several kinds of knowledge are required to understand and respond to a human's natural language commands and questions. If a person requests an assistant robot to take me to Alice's office, the robot must know that Alice is a person who owns some unique office, and that take me means it should navigate there. Similarly, if a person requests bring me the heavy, green mug, the robot must have accurate mental models of the physical concepts heavy, green, and mug. To avoid forcing humans to use key phrases or words robots already know, this thesis focuses on helping robots understanding new language constructs through interactions with humans and with the world around them. To understand a command in natural language, a robot must first convert that command to an internal representation that it can reason with. Semantic parsing is a method for performing this conversion, and the target representation is often semantic forms represented as predicate logic with lambda calculus. Traditional semantic parsing relies on hand-crafted resources from a human expert: an ontology of concepts, a lexicon connecting language to those concepts, and training examples of language with abstract meanings. One thrust of this thesis is to perform semantic parsing with sparse initial data. We use the conversations between a robot and human users to induce pairs of natural language utterances with the target semantic forms a robot discovers through its questions, reducing the annotation effort of creating training examples for parsing. We use this data to build more dialog-capable robots in new domains with much less expert human effort (Thomason et al., 2015; Padmakumar et al., 2017). Meanings of many language concepts are bound to the physical world. Understanding object properties and categories, such as heavy, green, and mug requires interacting with and perceiving the physical world. Embodied robots can use manipulation capabilities, such as pushing, picking up, and dropping objects to gather sensory data about them. This data can be used to understand non-visual concepts like heavy and empty (e.g. get the empty carton of milk from the fridge), and assist with concepts that have both visual and non-visual expression (e.g. tall things look big and also exert force sooner than short things when pressed down on). A second thrust of this thesis focuses on strategies for learning these concepts using multi-modal sensory information. We use human-in-the-loop learning to get labels between concept words and actual objects in the environment (Thomason et al., 2016, 2017). We also explore ways to tease out polysemy and synonymy in concept words (Thomason and Mooney, 2017) such as light, which can refer to a weight or a color, the latter sense being synonymous with pale. Additionally, pushing, picking up, and dropping objects to gather sensory information is prohibitively time-consuming, so we investigate strategies for using linguistic information and human input to expedite exploration when learning a new concept (Thomason et al., 2018). Finally, we build an integrated agent with both parsing and perception capabilities that learns from conversations with users to improve both components over time. We demonstrate that parser learning from conversations (Thomason et al., 2015) can be combined with multi-modal perception (Thomason et al., 2016) using predicate-object labels gathered through opportunistic active learning (Thomason et al., 2017) during those conversations to improve performance for understanding natural language commands from humans. Human users also qualitatively rate this integrated learning agent as more usable after it has improved from conversation-based learning.Computer Science
A Survey of Knowledge-based Sequential Decision Making under Uncertainty
Reasoning with declarative knowledge (RDK) and sequential decision-making
(SDM) are two key research areas in artificial intelligence. RDK methods reason
with declarative domain knowledge, including commonsense knowledge, that is
either provided a priori or acquired over time, while SDM methods
(probabilistic planning and reinforcement learning) seek to compute action
policies that maximize the expected cumulative utility over a time horizon;
both classes of methods reason in the presence of uncertainty. Despite the rich
literature in these two areas, researchers have not fully explored their
complementary strengths. In this paper, we survey algorithms that leverage RDK
methods while making sequential decisions under uncertainty. We discuss
significant developments, open problems, and directions for future work
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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
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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