1,021 research outputs found
Chameleons in imagined conversations: A new approach to understanding coordination of linguistic style in dialogs
Conversational participants tend to immediately and unconsciously adapt to
each other's language styles: a speaker will even adjust the number of articles
and other function words in their next utterance in response to the number in
their partner's immediately preceding utterance. This striking level of
coordination is thought to have arisen as a way to achieve social goals, such
as gaining approval or emphasizing difference in status. But has the adaptation
mechanism become so deeply embedded in the language-generation process as to
become a reflex? We argue that fictional dialogs offer a way to study this
question, since authors create the conversations but don't receive the social
benefits (rather, the imagined characters do). Indeed, we find significant
coordination across many families of function words in our large movie-script
corpus. We also report suggestive preliminary findings on the effects of gender
and other features; e.g., surprisingly, for articles, on average, characters
adapt more to females than to males.Comment: data available at http://www.cs.cornell.edu/~cristian/movie
Automatic Movie Abstracting
Presented is an algorithm for automatic production of a video abstract of a feature film, similar to a movietrailer. It selects clips from the original movie based on detection of special events like dialogs, shots, explosions and text occurrences, and on general action indicators applied to scenes. These clips are then assembled to form a video trailer using a model of editing. Additional clips, audio pieces, images and text, which are also retrieved from the original video for their content, are added to produce a multimedia abstract. The collection of multime dia objects is presented on an HTML-page
Inferring Narrative Causality between Event Pairs in Films
To understand narrative, humans draw inferences about the underlying
relations between narrative events. Cognitive theories of narrative
understanding define these inferences as four different types of causality,
that include pairs of events A, B where A physically causes B (X drop, X
break), to pairs of events where A causes emotional state B (Y saw X, Y felt
fear). Previous work on learning narrative relations from text has either
focused on "strict" physical causality, or has been vague about what relation
is being learned. This paper learns pairs of causal events from a corpus of
film scene descriptions which are action rich and tend to be told in
chronological order. We show that event pairs induced using our methods are of
high quality and are judged to have a stronger causal relation than event pairs
from Rel-grams
From my pen to your ears: automatic production of radio plays from unstructured story text
A radio play is a form of drama which exists in the acoustic domain and is usually consumed over broadcast radio. In this paper a method is proposed that, given a story in the form of unstructured text, produces a radio play that tells this story. First, information about characters, acting lines, and environments is retrieved from the text. The information extracted serves to generate a production script which can be used either by producers of radiodrama, or subsequently used to automatically generate the radio play as an audio file. The system is evaluated in two parts: precision, recall, and f1 scores are computed for the information retrieval part while multistimulus listening tests are used for subjective evaluation of the generated audio
Augmented robotics dialog system for enhancing human-robot interaction
Augmented reality, augmented television and second screen are cutting edge technologies that provide end users extra and enhanced information related to certain events in real time. This enriched information helps users better understand such events, at the same time providing a more satisfactory experience. In the present paper, we apply this main idea to human-robot interaction (HRI), to how users and robots interchange information. The ultimate goal of this paper is to improve the quality of HRI, developing a new dialog manager system that incorporates enriched information from the semantic web. This work presents the augmented robotic dialog system (ARDS), which uses natural language understanding mechanisms to provide two features: (i) a non-grammar multimodal input (verbal and/or written) text; and (ii) a contextualization of the information conveyed in the interaction. This contextualization is achieved by information enrichment techniques that link the extracted information from the dialog with extra information about the world available in semantic knowledge bases. This enriched or contextualized information (information enrichment, semantic enhancement or contextualized information are used interchangeably in the rest of this paper) offers many possibilities in terms of HRI. For instance, it can enhance the robot's pro-activeness during a human-robot dialog (the enriched information can be used to propose new topics during the dialog, while ensuring a coherent interaction). Another possibility is to display additional multimedia content related to the enriched information on a visual device. This paper describes the ARDS and shows a proof of concept of its applications.The authors gratefully acknowledge the funds provided by the Spanish MICINN (Ministry of Science and Innovation) through the project “Aplicaciones de los robots sociales”, DPI2011-26980 from the Spanish Ministry of Economy and Competitiveness. The research leading to these results has received funding from the RoboCity2030-III-CM project (Robótica aplicada a la mejora de la calidad de vida de los ciudadanos. fase III; S2013/MIT-2748), funded by Programas de Actividades I+D en la Comunidad de Madrid and co-funded by the Structural Funds of the EU
A Survey of Personality, Persona, and Profile in Conversational Agents and Chatbots
We present a review of personality in neural conversational agents (CAs),
also called chatbots. First, we define Personality, Persona, and Profile. We
explain all personality schemes which have been used in CAs, and list models
under the scheme(s) which they use. Second we describe 21 datasets which have
been developed in recent CA personality research. Third, we define the methods
used to embody personality in a CA, and review recent models using them.
Fourth, we survey some relevant reviews on CAs, personality, and related
topics. Finally, we draw conclusions and identify some research challenges for
this important emerging field.Comment: 25 pages, 6 tables, 207 reference
Learning From Free-Text Human Feedback -- Collect New Datasets Or Extend Existing Ones?
Learning from free-text human feedback is essential for dialog systems, but
annotated data is scarce and usually covers only a small fraction of error
types known in conversational AI. Instead of collecting and annotating new
datasets from scratch, recent advances in synthetic dialog generation could be
used to augment existing dialog datasets with the necessary annotations.
However, to assess the feasibility of such an effort, it is important to know
the types and frequency of free-text human feedback included in these datasets.
In this work, we investigate this question for a variety of commonly used
dialog datasets, including MultiWoZ, SGD, BABI, PersonaChat,
Wizards-of-Wikipedia, and the human-bot split of the Self-Feeding Chatbot.
Using our observations, we derive new taxonomies for the annotation of
free-text human feedback in dialogs and investigate the impact of including
such data in response generation for three SOTA language generation models,
including GPT-2, LLAMA, and Flan-T5. Our findings provide new insights into the
composition of the datasets examined, including error types, user response
types, and the relations between them.Comment: Accepted to be presented at EMNLP 202
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