4,184 research outputs found
Follow-up question handling in the IMIX and Ritel systems: A comparative study
One of the basic topics of question answering (QA) dialogue systems is how follow-up questions should be interpreted by a QA system. In this paper, we shall discuss our experience with the IMIX and Ritel systems, for both of which a follow-up question handling scheme has been developed, and corpora have been collected. These two systems are each other's opposites in many respects: IMIX is multimodal, non-factoid, black-box QA, while Ritel is speech, factoid, keyword-based QA. Nevertheless, we will show that they are quite comparable, and that it is fruitful to examine the similarities and differences. We shall look at how the systems are composed, and how real, non-expert, users interact with the systems. We shall also provide comparisons with systems from the literature where possible, and indicate where open issues lie and in what areas existing systems may be improved. We conclude that most systems have a common architecture with a set of common subtasks, in particular detecting follow-up questions and finding referents for them. We characterise these tasks using the typical techniques used for performing them, and data from our corpora. We also identify a special type of follow-up question, the discourse question, which is asked when the user is trying to understand an answer, and propose some basic methods for handling it
Generating multimedia presentations: from plain text to screenplay
In many Natural Language Generation (NLG) applications, the output is limited to plain text – i.e., a string of words with punctuation and paragraph breaks, but no indications for layout, or pictures, or dialogue. In several projects, we have begun to explore NLG applications in which these extra media are brought into play. This paper gives an informal account of what we have learned. For coherence, we focus on the domain of patient information leaflets, and follow an example in which the same content is expressed first in plain text, then in formatted text, then in text with pictures, and finally in a dialogue script that can be performed by two animated agents. We show how the same meaning can be mapped to realisation patterns in different media, and how the expanded options for expressing meaning are related to the perceived style and tone of the presentation. Throughout, we stress that the extra media are not simple added to plain text, but integrated with it: thus the use of formatting, or pictures, or dialogue, may require radical rewording of the text itself
On the Development of Adaptive and User-Centred Interactive Multimodal Interfaces
Multimodal systems have attained increased attention in recent years, which has made possible important
improvements in the technologies for recognition, processing, and generation of multimodal information.
However, there are still many issues related to multimodality which are not clear, for example, the
principles that make it possible to resemble human-human multimodal communication. This chapter
focuses on some of the most important challenges that researchers have recently envisioned for future
multimodal interfaces. It also describes current efforts to develop intelligent, adaptive, proactive, portable
and affective multimodal interfaces
From Knowledge Augmentation to Multi-tasking: Towards Human-like Dialogue Systems
The goal of building dialogue agents that can converse with humans naturally
has been a long-standing dream of researchers since the early days of
artificial intelligence. The well-known Turing Test proposed to judge the
ultimate validity of an artificial intelligence agent on the
indistinguishability of its dialogues from humans'. It should come as no
surprise that human-level dialogue systems are very challenging to build. But,
while early effort on rule-based systems found limited success, the emergence
of deep learning enabled great advance on this topic.
In this thesis, we focus on methods that address the numerous issues that
have been imposing the gap between artificial conversational agents and
human-level interlocutors. These methods were proposed and experimented with in
ways that were inspired by general state-of-the-art AI methodologies. But they
also targeted the characteristics that dialogue systems possess.Comment: PhD thesi
A Unified Framework for Slot based Response Generation in a Multimodal Dialogue System
Natural Language Understanding (NLU) and Natural Language Generation (NLG)
are the two critical components of every conversational system that handles the
task of understanding the user by capturing the necessary information in the
form of slots and generating an appropriate response in accordance with the
extracted information. Recently, dialogue systems integrated with complementary
information such as images, audio, or video have gained immense popularity. In
this work, we propose an end-to-end framework with the capability to extract
necessary slot values from the utterance and generate a coherent response,
thereby assisting the user to achieve their desired goals in a multimodal
dialogue system having both textual and visual information. The task of
extracting the necessary information is dependent not only on the text but also
on the visual cues present in the dialogue. Similarly, for the generation, the
previous dialog context comprising multimodal information is significant for
providing coherent and informative responses. We employ a multimodal
hierarchical encoder using pre-trained DialoGPT and also exploit the knowledge
base (Kb) to provide a stronger context for both the tasks. Finally, we design
a slot attention mechanism to focus on the necessary information in a given
utterance. Lastly, a decoder generates the corresponding response for the given
dialogue context and the extracted slot values. Experimental results on the
Multimodal Dialogue Dataset (MMD) show that the proposed framework outperforms
the baselines approaches in both the tasks. The code is available at
https://github.com/avinashsai/slot-gpt.Comment: Published in the journal Multimedia Tools and Application
CFN-ESA: A Cross-Modal Fusion Network with Emotion-Shift Awareness for Dialogue Emotion Recognition
Multimodal Emotion Recognition in Conversation (ERC) has garnered growing
attention from research communities in various fields. In this paper, we
propose a cross-modal fusion network with emotion-shift awareness (CFN-ESA) for
ERC. Extant approaches employ each modality equally without distinguishing the
amount of emotional information, rendering it hard to adequately extract
complementary and associative information from multimodal data. To cope with
this problem, in CFN-ESA, textual modalities are treated as the primary source
of emotional information, while visual and acoustic modalities are taken as the
secondary sources. Besides, most multimodal ERC models ignore emotion-shift
information and overfocus on contextual information, leading to the failure of
emotion recognition under emotion-shift scenario. We elaborate an emotion-shift
module to address this challenge. CFN-ESA mainly consists of the unimodal
encoder (RUME), cross-modal encoder (ACME), and emotion-shift module (LESM).
RUME is applied to extract conversation-level contextual emotional cues while
pulling together the data distributions between modalities; ACME is utilized to
perform multimodal interaction centered on textual modality; LESM is used to
model emotion shift and capture related information, thereby guide the learning
of the main task. Experimental results demonstrate that CFN-ESA can effectively
promote performance for ERC and remarkably outperform the state-of-the-art
models.Comment: 13 pages, 10 figure
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