5,319 research outputs found
Transition-based directed graph construction for emotion-cause pair extraction
Emotion-cause pair extraction aims to extract all potential pairs of emotions and corresponding causes from unannotated emotion text. Most existing methods are pipelined framework, which identifies emotions and extracts causes separately, leading to a drawback of error propagation. Towards this issue, we propose a transition-based model to transform the task into a procedure of parsing-like directed graph construction. The proposed model incrementally generates the directed graph with labeled edges based on a sequence of actions, from which we can recognize emotions with the corresponding causes simultaneously, thereby optimizing separate subtasks jointly and maximizing mutual benefits of tasks interdependently. Experimental results show that our approach achieves the best performance, outperforming the state-of-the-art methods by 6.71% (p<0.01) in F1 measure
An Emotion Type Informed Multi-Task Model for Emotion Cause Pair Extraction
Emotion-Cause Pair Extraction (ECPE) aims to jointly extract emotion clauses and the corresponding cause clauses from a document, which is important for user evaluation or public opinion analysis. Existing research addresses the ECPE task through a two-step or an end-to-end approach. Although previous work shows promising performances, they suffer from two limitations: 1) they fail to take full advantage of emotion type information, which has advantages for modelling the dependencies between emotion and cause clauses from a semantic perspective; 2) they ignored the interaction between local and global information, which is important for ECPE. To address these issues, we propose an ECPE Pair Generator (ECPE-PG), with a Clause-Encoder layer, a Pre-Output layer and an Information Interaction-based Pair Generation (IIPG) Module embedded. This model first encodes clauses into vector representations through the Clause-Encoder layer and then preforms emotion clause extraction (EE), cause clause extraction (CE) and emotion type extraction (ETE), respectively, through the Pre-Output layer, on the basis of which the IIPG module analyzes the complex emotional logic of relationships between clauses and estimates the candidate pairs based on the interaction of global and local information. It should be noted that emotion type information is regarded as a crucial indication in the IIPG module to assist the identification of emotion-cause pairs. Experimental results show that our method outperforms the state-of-the-art methods on benchmark datasets
A Multi-turn Machine Reading Comprehension Framework with Rethink Mechanism for Emotion-Cause Pair Extraction
Emotion-cause pair extraction (ECPE) is an emerging task in emotion cause
analysis, which extracts potential emotion-cause pairs from an emotional
document. Most recent studies use end-to-end methods to tackle the ECPE task.
However, these methods either suffer from a label sparsity problem or fail to
model complicated relations between emotions and causes. Furthermore, they all
do not consider explicit semantic information of clauses. To this end, we
transform the ECPE task into a document-level machine reading comprehension
(MRC) task and propose a Multi-turn MRC framework with Rethink mechanism
(MM-R). Our framework can model complicated relations between emotions and
causes while avoiding generating the pairing matrix (the leading cause of the
label sparsity problem). Besides, the multi-turn structure can fuse explicit
semantic information flow between emotions and causes. Extensive experiments on
the benchmark emotion cause corpus demonstrate the effectiveness of our
proposed framework, which outperforms existing state-of-the-art methods.Comment: Accepted to COLING 202
TSAM: A Two-Stream Attention Model for Causal Emotion Entailment
Causal Emotion Entailment (CEE) aims to discover the potential causes behind
an emotion in a conversational utterance. Previous works formalize CEE as
independent utterance pair classification problems, with emotion and speaker
information neglected. From a new perspective, this paper considers CEE in a
joint framework. We classify multiple utterances synchronously to capture the
correlations between utterances in a global view and propose a Two-Stream
Attention Model (TSAM) to effectively model the speaker's emotional influences
in the conversational history. Specifically, the TSAM comprises three modules:
Emotion Attention Network (EAN), Speaker Attention Network (SAN), and
interaction module. The EAN and SAN incorporate emotion and speaker information
in parallel, and the subsequent interaction module effectively interchanges
relevant information between the EAN and SAN via a mutual BiAffine
transformation. Extensive experimental results demonstrate that our model
achieves new State-Of-The-Art (SOTA) performance and outperforms baselines
remarkably
Multi-Task Learning Framework for Extracting Emotion Cause Span and Entailment in Conversations
Predicting emotions expressed in text is a well-studied problem in the NLP
community. Recently there has been active research in extracting the cause of
an emotion expressed in text. Most of the previous work has done causal emotion
entailment in documents. In this work, we propose neural models to extract
emotion cause span and entailment in conversations. For learning such models,
we use RECCON dataset, which is annotated with cause spans at the utterance
level. In particular, we propose MuTEC, an end-to-end Multi-Task learning
framework for extracting emotions, emotion cause, and entailment in
conversations. This is in contrast to existing baseline models that use ground
truth emotions to extract the cause. MuTEC performs better than the baselines
for most of the data folds provided in the dataset.Comment: 19 Pages, Accepted at Workshop on Transfer Learning for Natural
Language Processing, NeurIPS 202
A COMPUTATION METHOD/FRAMEWORK FOR HIGH LEVEL VIDEO CONTENT ANALYSIS AND SEGMENTATION USING AFFECTIVE LEVEL INFORMATION
VIDEO segmentation facilitates e±cient video indexing and navigation in large
digital video archives. It is an important process in a content-based video
indexing and retrieval (CBVIR) system. Many automated solutions performed seg-
mentation by utilizing information about the \facts" of the video. These \facts"
come in the form of labels that describe the objects which are captured by the cam-
era. This type of solutions was able to achieve good and consistent results for some
video genres such as news programs and informational presentations. The content
format of this type of videos is generally quite standard, and automated solutions
were designed to follow these format rules. For example in [1], the presence of news
anchor persons was used as a cue to determine the start and end of a meaningful
news segment.
The same cannot be said for video genres such as movies and feature films.
This is because makers of this type of videos utilized different filming techniques to
design their videos in order to elicit certain affective response from their targeted
audience. Humans usually perform manual video segmentation by trying to relate
changes in time and locale to discontinuities in meaning [2]. As a result, viewers
usually have doubts about the boundary locations of a meaningful video segment
due to their different affective responses.
This thesis presents an entirely new view to the problem of high level video
segmentation. We developed a novel probabilistic method for affective level video
content analysis and segmentation. Our method had two stages. In the first stage,
a®ective content labels were assigned to video shots by means of a dynamic bayesian
0. Abstract 3
network (DBN). A novel hierarchical-coupled dynamic bayesian network (HCDBN)
topology was proposed for this stage. The topology was based on the pleasure-
arousal-dominance (P-A-D) model of a®ect representation [3]. In principle, this
model can represent a large number of emotions. In the second stage, the visual,
audio and a®ective information of the video was used to compute a statistical feature
vector to represent the content of each shot. Affective level video segmentation was
achieved by applying spectral clustering to the feature vectors.
We evaluated the first stage of our proposal by comparing its emotion detec-
tion ability with all the existing works which are related to the field of a®ective video
content analysis. To evaluate the second stage, we used the time adaptive clustering
(TAC) algorithm as our performance benchmark. The TAC algorithm was the best
high level video segmentation method [2]. However, it is a very computationally
intensive algorithm. To accelerate its computation speed, we developed a modified
TAC (modTAC) algorithm which was designed to be mapped easily onto a field
programmable gate array (FPGA) device. Both the TAC and modTAC algorithms
were used as performance benchmarks for our proposed method.
Since affective video content is a perceptual concept, the segmentation per-
formance and human agreement rates were used as our evaluation criteria. To obtain
our ground truth data and viewer agreement rates, a pilot panel study which was
based on the work of Gross et al. [4] was conducted. Experiment results will show
the feasibility of our proposed method. For the first stage of our proposal, our
experiment results will show that an average improvement of as high as 38% was
achieved over previous works. As for the second stage, an improvement of as high
as 37% was achieved over the TAC algorithm
Designing interactive virtual environments with feedback in health applications.
One of the most important factors to influence user experience in human-computer interaction is the user emotional reaction. Interactive environments including serious games that are responsive to user emotions improve their effectiveness and user satisfactions. Testing and training for user emotional competence is meaningful in healthcare field, which has motivated us to analyze immersive affective games using emotional feedbacks. In this dissertation, a systematic model of designing interactive environment is presented, which consists of three essential modules: affect modeling, affect recognition, and affect control. In order to collect data for analysis and construct these modules, a series of experiments were conducted using virtual reality (VR) to evoke user emotional reactions and monitoring the reactions by physiological data. The analysis results lead to the novel approach of a framework to design affective gaming in virtual reality, including the descriptions on the aspects of interaction mechanism, graph-based structure, and user modeling. Oculus Rift was used in the experiments to provide immersive virtual reality with affective scenarios, and a sample application was implemented as cross-platform VR physical training serious game for elderly people to demonstrate the essential parts of the framework. The measurements of playability and effectiveness are discussed. The introduced framework should be used as a guiding principle for designing affective VR serious games. Possible healthcare applications include emotion competence training, educational softwares, as well as therapy methods
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