33 research outputs found
Automatic Segmentation of Multiparty Dialogue
In this paper, we investigate the problem of automatically predicting segment boundaries in spoken multiparty dialogue. We extend prior work in two ways. We first apply approaches that have been proposed for predicting top-level topic shifts to the problem of identifying subtopic boundaries. We then explore the impact on performance of using ASR output as opposed to human transcription. Examination of the effect of features shows that predicting top-level and predicting subtopic boundaries are two distinct tasks: (1) for predicting subtopic boundaries, the lexical cohesion-based approach alone can achieve competitive results, (2) for predicting top-level boundaries, the machine learning approach that combines lexical-cohesion and conversational features performs best, and (3) conversational cues, such as cue phrases and overlapping speech, are better indicators for the top-level prediction task. We also find that the transcription errors inevitable in ASR output have a negative impact on models that combine lexical-cohesion and conversational features, but do not change the general preference of approach for the two tasks
Meeting decision detection: multimodal information fusion for multi-party dialogue understanding
Modern advances in multimedia and storage technologies have led to huge archives
of human conversations in widely ranging areas. These archives offer a wealth of information
in the organization contexts. However, retrieving and managing information
in these archives is a time-consuming and labor-intensive task. Previous research applied
keyword and computer vision-based methods to do this. However, spontaneous
conversations, complex in the use of multimodal cues and intricate in the interactions
between multiple speakers, have posed new challenges to these methods. We need
new techniques that can leverage the information hidden in multiple communication
modalities – including not just “what” the speakers say but also “how” they express
themselves and interact with others.
In responding to this need, the thesis inquires into the multimodal nature of meeting
dialogues and computational means to retrieve and manage the recorded meeting
information. In particular, this thesis develops the Meeting Decision Detector (MDD)
to detect and track decisions, one of the most important outcomes of the meetings.
The MDD involves not only the generation of extractive summaries pertaining to the
decisions (“decision detection”), but also the organization of a continuous stream of
meeting speech into locally coherent segments (“discourse segmentation”).
This inquiry starts with a corpus analysis which constitutes a comprehensive empirical
study of the decision-indicative and segment-signalling cues in the meeting
corpora. These cues are uncovered from a variety of communication modalities, including
the words spoken, gesture and head movements, pitch and energy level, rate
of speech, pauses, and use of subjective terms. While some of the cues match the
previous findings of speech segmentation, some others have not been studied before.
The analysis also provides empirical grounding for computing features and integrating
them into a computational model. To handle the high-dimensional multimodal
feature space in the meeting domain, this thesis compares empirically feature discriminability
and feature pattern finding criteria. As the different knowledge sources are
expected to capture different types of features, the thesis also experiments with methods
that can harness synergy between the multiple knowledge sources.
The problem formalization and the modeling algorithm so far correspond to an
optimal setting: an off-line, post-meeting analysis scenario. However, ultimately the
MDD is expected to be operated online – right after a meeting, or when a meeting
is still in progress. Thus this thesis also explores techniques that help relax the optimal
setting, especially those using only features that can be generated with a higher
degree of automation. Empirically motivated experiments are designed to handle the
corresponding performance degradation.
Finally, with the users in mind, this thesis evaluates the use of query-focused summaries
in a decision debriefing task, which is common in the organization context. The
decision-focused extracts (which represent compressions of 1%) is compared against
the general-purpose extractive summaries (which represent compressions of 10-40%).
To examine the effect of model automation on the debriefing task, this evaluation experiments
with three versions of decision-focused extracts, each relaxing one manual
annotation constraint. Task performance is measured in actual task effectiveness, usergenerated
report quality, and user-perceived success. The users’ clicking behaviors are
also recorded and analyzed to understand how the users leverage the different versions
of extractive summaries to produce abstractive summaries.
The analysis framework and computational means developed in this work is expected
to be useful for the creation of other dialogue understanding applications, especially
those that require to uncover the implicit semantics of meeting dialogues
Combining Multiple Knowledge Sources for Dialogue Segmentation in Multimedia Archives
Automatic segmentation is important for
making multimedia archives comprehensible,
and for developing downstream information
retrieval and extraction modules. In
this study, we explore approaches that can
segment multiparty conversational speech
by integrating various knowledge sources
(e.g., words, audio and video recordings,
speaker intention and context). In particular,
we evaluate the performance of a Maximum
Entropy approach, and examine the
effectiveness of multimodal features on the
task of dialogue segmentation. We also provide
a quantitative account of the effect of
using ASR transcription as opposed to human
transcripts
Automatic topic segmentation and labeling in multiparty dialogue
This study concerns how to segment a scenario-driven multiparty
dialogue and how to label these segments automatically.
We apply approaches that have been proposed for identifying
topic boundaries at a coarser level to the problem of
identifying agenda-based topic boundaries in scenario-based
meetings. We also develop conditional models to classify segments
into topic classes. Experiments in topic segmentation
show that a supervised classification approach that combines
lexical and conversational features outperforms the unsupervised
lexical chain-based approach, achieving 20% and 12%
improvement on segmentating top-level and sub-topic segments
respectively. Experiments in topic classification suggest
that it is possible to automatically categorize segments
into appropriate topic classes given only the transcripts. Training
with features selected using the Log Likelihood ratio improves
the results by 13.3%
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A First Step Towards Behavioral Coaching for Managing Stress: A Case Study on Optimal Policy Estimation with Multi-stage Threshold Q-learning
Psychological stress is a major contributor to the adoption of unhealthy behaviors, which in turn accounts for 41% of global cardiovascular disease burden. While the proliferation of mobile health apps has offered promise to stress management, these apps do not provide micro-level feedback with regard to how to adjust one's behaviors to achieve a desired health outcome. In this paper, we formulate the task of multi-stage stress management as a sequential decision-making problem and explore the application of reinforcement learning to provide micro-level feedback for stress reduction. Specifically, we incorporate a multi-stage threshold selection into Q-learning to derive an interpretable form of a recommendation policy for behavioral coaching. We apply this method on an observational dataset that contains Fitbit ActiGraph measurements and self-reported stress levels. The estimated policy is then used to understand how exercise patterns may affect users' psychological stress levels and to perform coaching more effectively
Serotype Competence and Penicillin Resistance in Streptococcus pneumoniae
Enhanced molecular surveillance of virulent clones with higher competence can detect serotype switching
Serotonin receptor HTR6-mediated mTORC1 signaling regulates dietary restriction-induced memory enhancement
Dietary restriction (DR; sometimes called calorie restriction) has profound beneficial effects on physiological, psychological, and behavioral outcomes in animals and in humans. We have explored the molecular mechanism of DR-induced memory enhancement and demonstrate that dietary tryptophan-a precursor amino acid for serotonin biosynthesis in the brain-and serotonin receptor 5-hydroxytryptamine receptor 6 (HTR6) are crucial in mediating this process. We show that HTR6 inactivation diminishes DR-induced neurological alterations, including reduced dendritic complexity, increased spine density, and enhanced long-term potentiation (LTP) in hippocampal neurons. Moreover, we find that HTR6-mediated mechanistic target of rapamycin complex 1 (mTORC1) signaling is involved in DR-induced memory improvement. Our results suggest that the HTR6-mediated mTORC1 pathway may function as a nutrient sensor in hippocampal neurons to couple memory performance to dietary intake
Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition)
In 2008 we published the first set of guidelines for standardizing research in autophagy. Since then, research on this topic has continued to accelerate, and many new scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Accordingly, it is important to update these guidelines for monitoring autophagy in different organisms. Various reviews have described the range of assays that have been used for this purpose. Nevertheless, there continues to be confusion regarding acceptable methods to measure autophagy, especially in multicellular eukaryotes. For example, a key point that needs to be emphasized is that there is a difference between measurements that monitor the numbers or volume of autophagic elements (e.g., autophagosomes or autolysosomes) at any stage of the autophagic process versus those that measure fl ux through the autophagy pathway (i.e., the complete process including the amount and rate of cargo sequestered and degraded). In particular, a block in macroautophagy that results in autophagosome accumulation must be differentiated from stimuli that increase autophagic activity, defi ned as increased autophagy induction coupled with increased delivery to, and degradation within, lysosomes (inmost higher eukaryotes and some protists such as Dictyostelium ) or the vacuole (in plants and fungi). In other words, it is especially important that investigators new to the fi eld understand that the appearance of more autophagosomes does not necessarily equate with more autophagy. In fact, in many cases, autophagosomes accumulate because of a block in trafficking to lysosomes without a concomitant change in autophagosome biogenesis, whereas an increase in autolysosomes may reflect a reduction in degradative activity. It is worth emphasizing here that lysosomal digestion is a stage of autophagy and evaluating its competence is a crucial part of the evaluation of autophagic flux, or complete autophagy. Here, we present a set of guidelines for the selection and interpretation of methods for use by investigators who aim to examine macroautophagy and related processes, as well as for reviewers who need to provide realistic and reasonable critiques of papers that are focused on these processes. These guidelines are not meant to be a formulaic set of rules, because the appropriate assays depend in part on the question being asked and the system being used. In addition, we emphasize that no individual assay is guaranteed to be the most appropriate one in every situation, and we strongly recommend the use of multiple assays to monitor autophagy. Along these lines, because of the potential for pleiotropic effects due to blocking autophagy through genetic manipulation it is imperative to delete or knock down more than one autophagy-related gene. In addition, some individual Atg proteins, or groups of proteins, are involved in other cellular pathways so not all Atg proteins can be used as a specific marker for an autophagic process. In these guidelines, we consider these various methods of assessing autophagy and what information can, or cannot, be obtained from them. Finally, by discussing the merits and limits of particular autophagy assays, we hope to encourage technical innovation in the field