197,677 research outputs found
The MEANING Project
A pesar del progreso que se realiza en el Procesamiento del Lenguaje Natural (PLN) aún estamos lejos de la Comprensión del Lenguaje Natural. Un paso importante hacia este objetivo es el desarrollo de técnicas y recursos que traten conceptos en lugar de palabras. Sin embargo, si queremos construir la próxima generación de sistemas inteligentes que traten Tecnología de Lenguaje Humano en dominios abiertos necesitamos resolver dos tareas intermedias y complementarias: resolución de la ambigüedad léxica de las palabras y enriquecimiento automático y a gran escala de bases de conocimiento léxico.Progress is being made in Natural Language Processing (NLP) but there is still a long way towards Natural Language Understanding. An important step towards this goal is the development of technologies and resources that deal with concepts rather than words. However, to be able to build the next generation of intelligent open domain Human Language Technology (HLT) application systems we need to solve two complementary and intermediate tasks: Word Sense Disambiguation (WSD) and automatic large-scale enrichment of Lexical Knowledge Bases.The MEANING Project is funded by the EU 5th Framework IST Programme
ConfNet2Seq: Full Length Answer Generation from Spoken Questions
Conversational and task-oriented dialogue systems aim to interact with the
user using natural responses through multi-modal interfaces, such as text or
speech. These desired responses are in the form of full-length natural answers
generated over facts retrieved from a knowledge source. While the task of
generating natural answers to questions from an answer span has been widely
studied, there has been little research on natural sentence generation over
spoken content. We propose a novel system to generate full length natural
language answers from spoken questions and factoid answers. The spoken sequence
is compactly represented as a confusion network extracted from a pre-trained
Automatic Speech Recognizer. This is the first attempt towards generating
full-length natural answers from a graph input(confusion network) to the best
of our knowledge. We release a large-scale dataset of 259,788 samples of spoken
questions, their factoid answers and corresponding full-length textual answers.
Following our proposed approach, we achieve comparable performance with best
ASR hypothesis.Comment: Accepted at Text, Speech and Dialogue, 202
Evaluating Mixed-initiative Conversational Search Systems via User Simulation
Clarifying the underlying user information need by asking clarifying
questions is an important feature of modern conversational search system.
However, evaluation of such systems through answering prompted clarifying
questions requires significant human effort, which can be time-consuming and
expensive. In this paper, we propose a conversational User Simulator, called
USi, for automatic evaluation of such conversational search systems. Given a
description of an information need, USi is capable of automatically answering
clarifying questions about the topic throughout the search session. Through a
set of experiments, including automated natural language generation metrics and
crowdsourcing studies, we show that responses generated by USi are both inline
with the underlying information need and comparable to human-generated answers.
Moreover, we make the first steps towards multi-turn interactions, where
conversational search systems asks multiple questions to the (simulated) user
with a goal of clarifying the user need. To this end, we expand on currently
available datasets for studying clarifying questions, i.e., Qulac and ClariQ,
by performing a crowdsourcing-based multi-turn data acquisition. We show that
our generative, GPT2-based model, is capable of providing accurate and natural
answers to unseen clarifying questions in the single-turn setting and discuss
capabilities of our model in the multi-turn setting. We provide the code, data,
and the pre-trained model to be used for further research on the topic
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
I feel you: the design and evaluation of a domotic affect-sensitive spoken conversational agent
We describe the work on infusion of emotion into a limited-task autonomous spoken conversational agent situated in the domestic environment, using a need-inspired task-independent emotion model (NEMO). In order to demonstrate the generation of affect through the use of the model, we describe the work of integrating it with a natural-language mixed-initiative HiFi-control spoken conversational agent (SCA). NEMO and the host system communicate externally, removing the need for the Dialog Manager to be modified, as is done in most existing dialog systems, in order to be adaptive. The first part of the paper concerns the integration between NEMO and the host agent. The second part summarizes the work on automatic affect prediction, namely, frustration and contentment, from dialog features, a non-conventional source, in the attempt of moving towards a more user-centric approach. The final part reports the evaluation results obtained from a user study, in which both versions of the agent (non-adaptive and emotionally-adaptive) were compared. The results provide substantial evidences with respect to the benefits of adding emotion in a spoken conversational agent, especially in mitigating users' frustrations and, ultimately, improving their satisfaction
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