scholarly journals Graphs and Booleans: on the Generation of Referring Expressions

2008 ◽  
pp. 397-422 ◽  
Author(s):  
Kees Van Deemter ◽  
Emiel Krahmer
Keyword(s):  
Referring Expressions

Imperative in English Proverbs

2017 ◽  
Vol 8 (1) ◽  
pp. 75 ◽  
Author(s):  
Doc. Dr. Jelena Š. Novaković ◽  
PhD student Božana Tomić
Keyword(s):  
Personal Pronouns ◽  
Referring Expressions ◽  
Written Discourse ◽  
Special Regard

Apart from personal pronouns which are by far the most used referring expressions in English and Serbian, reference can be established and maintained using demonstratives.Their function is to refer to the location or distance of a person or an object. The aim of this paper is to examine reference realised by demonstratives with special regard to the restrictions written discourse imposes on their usage. The texts we used for analysis are narrative stories written in the two languages.

scholarly journals The influence of affect on the production of referring expressions

2021 ◽  
pp. 1-17
Author(s):  
Patrick Vonk ◽  
Martijn Goudbeek ◽  
Emiel Krahmer
Keyword(s):  
Referring Expressions

scholarly journals Comprehension of Referring Expressions in Chinese

1999 ◽  
Vol 14 (5-6) ◽  
pp. 715-743 ◽  
Author(s):  
Chin Lung Yang ◽  
Peter C. Gordon ◽  
Randall Hendrick ◽  
Jei Tun Wu
Keyword(s):  
Referring Expressions

scholarly journals Adaptive Generation in Dialogue Systems Using Dynamic User Modeling

2014 ◽  
Vol 40 (4) ◽  
pp. 883-920 ◽  
Author(s):  
Srinivasan Janarthanam ◽  
Oliver Lemon
Keyword(s):  
Domain Knowledge ◽  
User Modeling ◽  
Relative Difference ◽  
Dialogue Systems ◽  
User Profiles ◽  
Referring Expressions ◽  
Spoken Dialogue Systems ◽  
Content Selection ◽  
Adaptive Policies ◽  
User Knowledge

We address the problem of dynamically modeling and adapting to unknown users in resource-scarce domains in the context of interactive spoken dialogue systems. As an example, we show how a system can learn to choose referring expressions to refer to domain entities for users with different levels of domain expertise, and whose domain knowledge is initially unknown to the system. We approach this problem using a three step process: collecting data using a Wizard-of-Oz method, building simulated users, and learning to model and adapt to users using Reinforcement Learning techniques. We show that by using only a small corpus of non-adaptive dialogues and user knowledge profiles it is possible to learn an adaptive user modeling policy using a sense-predict-adapt approach. Our evaluation results show that the learned user modeling and adaptation strategies performed better in terms of adaptation than some simple hand-coded baseline policies, with both simulated and real users. With real users, the learned policy produced around a 20% increase in adaptation in comparison to an adaptive hand-coded baseline. We also show that adaptation to users' domain knowledge results in improving task success (99.47% for the learned policy vs. 84.7% for a hand-coded baseline) and reducing dialogue time of the conversation (11% relative difference). We also compared the learned policy to a variety of carefully hand-crafted adaptive policies that employ the user knowledge profiles to adapt their choices of referring expressions throughout a conversation. We show that the learned policy generalises better to unseen user profiles than these hand-coded policies, while having comparable performance on known user profiles. We discuss the overall advantages of this method and how it can be extended to other levels of adaptation such as content selection and dialogue management, and to other domains where adapting to users' domain knowledge is useful, such as travel and healthcare.

Multi-modal referring expressions in human-human task descriptions and their implications for human-robot interaction

2016 ◽  
Vol 17 (2) ◽  
pp. 180-210
Author(s):  
Stephanie Gross ◽  
Brigitte Krenn ◽  
Matthias Scheutz
Keyword(s):  
Language Processing ◽  
Eye Gaze ◽  
Human Robot Interaction ◽  
Referring Expressions ◽  
Visual Channel ◽  
Student Interactions ◽  
Seamless Integration ◽  
Teacher Student ◽  
Processing Module ◽  
Task Descriptions

Abstract Human instructors often refer to objects and actions involved in a task description using both linguistic and non-linguistic means of communication. Hence, for robots to engage in natural human-robot interactions, we need to better understand the various relevant aspects of human multi-modal task descriptions. We analyse reference resolution to objects in a data collection comprising two object manipulation tasks (22 teacher student interactions in Task 1 and 16 in Task 2) and find that 78.76% of all referring expressions to the objects relevant in Task 1 are verbally underspecified and 88.64% of all referring expressions are verbally underspecified in Task 2. The data strongly suggests that a language processing module for robots must be genuinely multi-modal, allowing for seamless integration of information transmitted in the verbal and the visual channel, whereby tracking the speaker’s eye gaze and gestures as well as object recognition are necessary preconditions.

scholarly journals Deep Learning Based BCI Control of a Robotic Service Assistant Using Intelligent Goal Formulation

2018 ◽  
Author(s):  
D. Kuhner ◽  
L.D.J. Fiederer ◽  
J. Aldinger ◽  
F. Burget ◽  
M. Völker ◽  
...  
Keyword(s):  
Deep Learning ◽  
User Study ◽  
Human Robot Interaction ◽  
Service Robots ◽  
Referring Expressions ◽  
Non Invasive ◽  
Neuronal Signal ◽  
High Level ◽  
User Friendly ◽  
Robotic Tasks

AbstractAs autonomous service robots become more affordable and thus available for the general public, there is a growing need for user-friendly interfaces to control these systems. Control interfaces typically get more complicated with increasing complexity of the robotic tasks and the environment. Traditional control modalities as touch, speech or gesture commands are not necessarily suited for all users. While non-expert users can make the effort to familiarize themselves with a robotic system, paralyzed users may not be capable of controlling such systems even though they need robotic assistance most. In this paper, we present a novel framework, that allows these users to interact with a robotic service assistant in a closed-loop fashion, using only thoughts. The system is composed of several interacting components: non-invasive neuronal signal recording and co-adaptive deep learning which form the brain-computer interface (BCI), high-level task planning based on referring expressions, navigation and manipulation planning as well as environmental perception. We extensively evaluate the BCI in various tasks, determine the performance of the goal formulation user interface and investigate its intuitiveness in a user study. Furthermore, we demonstrate the applicability and robustness of the system in real world scenarios, considering fetch-and-carry tasks and tasks involving human-robot interaction. As our results show, the system is capable of adapting to frequent changes in the environment and reliably accomplishes given tasks within a reasonable amount of time. Combined with high-level planning using referring expressions and autonomous robotic systems, interesting new perspectives open up for non-invasive BCI-based human-robot interactions.

Explicitation, simplification and neutralisation in the translation of reference

2020 ◽  
Vol 13 ◽  
pp. 29-43
Author(s):  
Mona ARHIRE
Keyword(s):  
Discourse Analysis ◽  
Qualitative Methods ◽  
Translation Studies ◽  
Referring Expressions ◽  
Training Environment ◽  
John Fowles ◽  
Characteristic Features ◽  
Quantitative And Qualitative Methods ◽  
Theoretical Component ◽  
Shed Light

Recurrent features of translation, sometimes labelled as ‘translation universals’, have been intensively investigated within Descriptive Corpus-based Translation Studies. Numerous language pairs have been set under researchers’ lens with a view to observing languages from a contrastive viewpoint, but also individually, in their translational manifestations. This has enabled the identification of characteristic features of the translational facets of languages, which have generated more and more nuanced scholarly theories. This paper examines the occurrence of some of the most frequent features of translation, namely: explicitation, simplification and neutralisation in the translation of reference as a cohesive device. Methodologically speaking, the investigation combines the theoretical and applied areas of Translation Studies, with an interdisciplinary dimension provided by the fusion of methodological input borrowed from Descriptive Translation Studies, Discourse Analysis and Contrastive Studies. The theoretical component of the research refers to issues of contrastiveness between English and Romanian viewed from a translational angle, in terms of equivalence and the occurrence of the three features of translation. The applied area of Translation Studies comprises the empirical approach to the translation of reference, while addressing not only the researchers’ community, but also the practitioners in translation and the translator training environment. The research applies both quantitative and qualitative methods to investigate the data selected from John Fowles’ novel Mantissa (1982) and its translation into Romanian by Angela Jianu (Fowles 1995). The findings provide insights into the nature and functions of referring expressions as formal links, but also as stylistic devices, and shed light into issues related to contrastiveness of reference between English and Romanian, to aspects of equivalence and translatability, as well as to the occurrence of translation universals.

Taking the perspective of narrative characters

2021 ◽  
Vol 38 ◽  
pp. 144-162
Author(s):  
Jorrig Vogels ◽  
Sofia Bimpikou ◽  
Owen Kapelle ◽  
Emar Maier
Keyword(s):  
Narrative Discourse ◽  
Mouse Tracking ◽  
Referring Expressions ◽  
Ongoing Debate ◽  
Web Based ◽  
Free Indirect Discourse ◽  
Indirect Discourse ◽  
Referential Processing ◽  
Narrative Characters ◽  
Processing Differences

Abstract An ongoing debate in the interpretation of referring expressions concerns the degree to which listeners make use of perspective information during referential processing. We aim to contribute to this debate by considering perspective shifting in narrative discourse. In a web-based mouse-tracking experiment in Dutch, we investigated whether listeners automatically shift to a narrative character’s perspective when resolving ambiguous referring expressions, and whether different linguistic perspective-shifting devices affect how and when listeners switch to another perspective. We compared perspective-neutral, direct, and free indirect discourse, manipulating which objects are visible to the character. Our results do not show a clear effect of the perspective shifting devices on participants’ eventual choice of referent, but our online mouse-tracking data reveal processing differences that suggest that listeners are indeed sensitive to the conventional markers of perspective shift associated with direct and (to a lesser degree) free indirect discourse.

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