96 research outputs found
PK-ICR: Persona-Knowledge Interactive Context Retrieval for Grounded Dialogue
Identifying relevant Persona or Knowledge for conversational systems is a
critical component of grounded dialogue response generation. However, each
grounding has been studied in isolation with more practical multi-context tasks
only recently introduced. We define Persona and Knowledge Dual Context
Identification as the task to identify Persona and Knowledge jointly for a
given dialogue, which could be of elevated importance in complex multi-context
Dialogue settings. We develop a novel grounding retrieval method that utilizes
all contexts of dialogue simultaneously while also requiring limited training
via zero-shot inference due to compatibility with neural Q \& A retrieval
models. We further analyze the hard-negative behavior of combining Persona and
Dialogue via our novel null-positive rank test
, , and to Ground: Designing User Persona-Aware Conversational Agents for Engaging Dialogue
This paper presents a method for building a personalized open-domain dialogue
system to address the (, , and
) problem for natural response generation in a commercial
setting, where personalized dialogue responses are heavily interleaved with
casual response turns. The proposed approach involves weighted dataset
blending, negative persona information augmentation methods, and the design of
personalized conversation datasets to address the challenges of
in personalized, open-domain dialogue systems. Our work effectively balances
dialogue fluency and tendency to ground, while also introducing a response-type
label to improve the controllability and explainability of the grounded
responses. The combination of these methods leads to more fluent conversations,
as evidenced by subjective human evaluations as well as objective evaluations.Comment: Accepted in ACL 2023 Industry Trac
Context-dependent Instruction Tuning for Dialogue Response Generation
Recent language models have achieved impressive performance in natural
language tasks by incorporating instructions with task input during
fine-tuning. Since all samples in the same natural language task can be
explained with the same task instructions, many instruction datasets only
provide a few instructions for the entire task, without considering the input
of each example in the task. However, this approach becomes ineffective in
complex multi-turn dialogue generation tasks, where the input varies highly
with each turn as the dialogue context changes, so that simple task
instructions cannot improve the generation performance. To address this
limitation, we introduce a context-based instruction fine-tuning framework for
each multi-turn dialogue which generates both responses and instructions based
on the previous context as input. During the evaluation, the model generates
instructions based on the previous context to self-guide the response. The
proposed framework produces comparable or even outstanding results compared to
the baselines by aligning instructions to the input during fine-tuning with the
instructions in quantitative evaluations on dialogue benchmark datasets with
reduced computation budget.Comment: Work in Progres
MIRACLE: Towards Personalized Dialogue Generation with Latent-Space Multiple Personal Attribute Control
Personalized dialogue systems aim to endow the chatbot agent with more
anthropomorphic traits for human-like interactions. Previous approaches have
explored explicitly user profile modeling using text descriptions, implicit
derivation of user embeddings, or utilizing handicraft prompts for ChatGPT-like
models. However, textual personas are limited in describing multi-faceted
attributes (\emph{e.g.}, \emph{language style, inner character nuances}),
implicit embedding suffers from personality sparsity, and handicraft prompts
lack fine-grained and stable controllability. Hence, these approaches may
struggle with complex personalized dialogue generation tasks that require
generating controllable responses with multiple personal attributes. To this
end, we propose \textbf{\textsc{Miracle}}, a novel personalized dialogue
generation method through \textbf{M}ult\textbf{I}ple Pe\textbf{R}sonal
\textbf{A}ttributes \textbf{C}ontrol within \textbf{L}atent-Space
\textbf{E}nergy-based Models. ttributes \textbf{C}ontrol within
\textbf{L}atent-Space \textbf{E}nergy-based Models. Specifically, our approach
first disentangles complex personality into multi-faceted attributes.
Subsequently, we employ a conditional variational auto-encoder to align with
the dense personalized responses within a latent joint attribute space. We have
also tailored a dedicated energy function and customized the ordinary
differential equations sampling method to offer flexible attribute composition
and precise attribute control. Extensive experiments demonstrate that
\textsc{Miracle} outperforms several strong baselines in terms of personality
controllability and response generation quality. Our dataset and code are
available at \url{https://github.com/LZY-the-boys/MIRACLE}Comment: Accepted by EMNLP2023 finding
Human Motion Generation: A Survey
Human motion generation aims to generate natural human pose sequences and
shows immense potential for real-world applications. Substantial progress has
been made recently in motion data collection technologies and generation
methods, laying the foundation for increasing interest in human motion
generation. Most research within this field focuses on generating human motions
based on conditional signals, such as text, audio, and scene contexts. While
significant advancements have been made in recent years, the task continues to
pose challenges due to the intricate nature of human motion and its implicit
relationship with conditional signals. In this survey, we present a
comprehensive literature review of human motion generation, which, to the best
of our knowledge, is the first of its kind in this field. We begin by
introducing the background of human motion and generative models, followed by
an examination of representative methods for three mainstream sub-tasks:
text-conditioned, audio-conditioned, and scene-conditioned human motion
generation. Additionally, we provide an overview of common datasets and
evaluation metrics. Lastly, we discuss open problems and outline potential
future research directions. We hope that this survey could provide the
community with a comprehensive glimpse of this rapidly evolving field and
inspire novel ideas that address the outstanding challenges.Comment: 20 pages, 5 figure
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
Modelling of photonic components based on ÷(3)nonlinear photonic crystals
En esta tesis se llevó a cabo un estudio de diversas propiedades de los cristales fotónicos 1D y 2D no lineales de tercer orden y de cómo se pueden aplicar dichas propiedades al desarrollo de dispositivos totalmente ópticos (por ejemplo, limitadores y conmutadores, compuertas lógicas, transistores ópticos, etc.). Se propuso una aproximación numérica para calcular las características básicas de los cristales fotónicos no lineales como, por ejemplo, el diagrama de bandas o la transmisión. La aproximación numérica presentada en la tesis tiene ciertas ventajas útiles para cualquiera que diseñe dispositivos ópticos basados en cristales fotónicos no lineales. El sofware desarrollado a base de esta aproximación numérica ha permitido diseñar y simular numéricamente un conmutador totalmente óptico cuyas prestaciones son superiores a las de dispositivos optoelectrónicos convencionales.This dissertation represents a summary of a study of different properties of 1D and 2D third-order nonlinear photonic crystals. It is shown how these properties can be utilized to develop various all-optical devices (e.g. optical limiters and switches, logical gates, optical transistors, etc.) In the dissertation, a novel numerical approximation has been proposed for analyzing the basic characteristics of the nonlinear photonic crystals like dispersion characteristics or transmittance curves. This numerical approximation possesses some important advantages useful in designing all-optical devices based on nonlinear photonic crystals. The software based on its algorithm has allowed to design and simulate a high-production all-optical switching device
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