1,801 research outputs found
Utilizing Review Summarization in a Spoken Recommendation System
In this paper we present a framework for spoken recommendation
systems. To provide reliable recommendations
to users, we incorporate a review summarization
technique which extracts informative opinion
summaries from grass-roots users‘ reviews. The dialogue
system then utilizes these review summaries to
support both quality-based opinion inquiry and feature-
specific entity search. We propose a probabilistic
language generation approach to automatically creating
recommendations in spoken natural language
from the text-based opinion summaries. A user study
in the restaurant domain shows that the proposed approaches
can effectively generate reliable and helpful
recommendations in human-computer conversations.T-Party ProjectQuanta Computer (Firm
Dialogue-Oriented Review Summary Generation for Spoken Dialogue Recommendation Systems
In this paper we present an opinion summarization technique in spoken dialogue systems. Opinion mining has been well studied for years, but very few have considered its application in spoken dialogue systems. Review summarization, when applied to real dialogue systems, is much more complicated than pure text-based summarization. We conduct a systematic study on dialogue-system-oriented review analysis and propose a three-level framework for a recommendation dialogue system. In previous work we have explored a linguistic parsing approach to phrase extraction from reviews. In this paper we will describe an approach using statistical models such as decision trees and SVMs to select the most representative phrases from the extracted phrase set. We will also explain how to generate informative yet concise review summaries for dialogue purposes. Experimental results in the restaurant domain show that the proposed approach using decision tree algorithms achieves an outperformance of 13% compared to SVM models and an improvement of 36% over a heuristic rule baseline. Experiments also show that the decision-tree-based phrase selection model can achieve rather reliable predictions on the phrase label, comparable to human judgment. The proposed statistical approach is based on domain-independent learning features and can be extended to other domains effectively
Examining the Effects of Using Picture-Based Summaries in a Flipped Classroom Model
The flipped classroom model allows more student-centered learning to take place within the classroom, and allows the teacher-centered lecture to be moved outside the classroom as a video viewed as homework. Studies suggest that student-centered learning is more effective than teacher-centered lecture; however, there is a paucity of research in creating effective at-home video lessons in the flipped classroom model. In this quasi-experimental quantitative study a picture-based summarization technique was incorporated into the Cornell note-taking strategy for students to use while viewing the at-home lecture video in a set of grade nine biology classes. A control group (n = 26) used the traditional Cornell note-taking strategy concluded with a student-generated written summary while viewing the at-home lecture, and a treatment group (n = 29) used the Cornell note-taking strategy, but was given a set of teacher-generated pictures as a summary of the at-home lecture. Results of an independent sample t-test generated from pre and post assessments using multiple-choice items showed no significant difference in comprehension of content objectives. Results of paired samples t-tests show that both control and treatment groups improved significantly over the four-week study. Therefore, it can be concluded that the use of picture-based summaries does not improve comprehension more than traditional written summarization, however, both strategies have a similar effect. It is suggested that future studies examine multiple aspects of student understanding to determine the effectiveness of teacher-generated picture-based summaries of Cornell notes on student comprehension
Large Language Models for Software Engineering: A Systematic Literature Review
Large Language Models (LLMs) have significantly impacted numerous domains,
notably including Software Engineering (SE). Nevertheless, a well-rounded
understanding of the application, effects, and possible limitations of LLMs
within SE is still in its early stages. To bridge this gap, our systematic
literature review takes a deep dive into the intersection of LLMs and SE, with
a particular focus on understanding how LLMs can be exploited in SE to optimize
processes and outcomes. Through a comprehensive review approach, we collect and
analyze a total of 229 research papers from 2017 to 2023 to answer four key
research questions (RQs). In RQ1, we categorize and provide a comparative
analysis of different LLMs that have been employed in SE tasks, laying out
their distinctive features and uses. For RQ2, we detail the methods involved in
data collection, preprocessing, and application in this realm, shedding light
on the critical role of robust, well-curated datasets for successful LLM
implementation. RQ3 allows us to examine the specific SE tasks where LLMs have
shown remarkable success, illuminating their practical contributions to the
field. Finally, RQ4 investigates the strategies employed to optimize and
evaluate the performance of LLMs in SE, as well as the common techniques
related to prompt optimization. Armed with insights drawn from addressing the
aforementioned RQs, we sketch a picture of the current state-of-the-art,
pinpointing trends, identifying gaps in existing research, and flagging
promising areas for future study
A Survey Paper on Ontology-Based Approaches for Semantic Data Mining
Semantic Data Mining alludes to the information mining assignments that deliberately consolidate area learning, particularly formal semantics, into the procedure. Numerous exploration endeavors have validated the advantages of fusing area learning in information mining and in the meantime, the expansion of information building has enhanced the group of space learning, particularly formal semantics and Semantic Web ontology. Ontology is an explicit specification of conceptualization and a formal approach to characterize the semantics of information and data. The formal structure of ontology makes it a nature approach to encode area information for the information mining utilization. Here in Semantic information mining ontology can possibly help semantic information mining and how formal semantics in ontologies can be joined into the data mining procedure.
DOI: 10.17762/ijritcc2321-8169.16048
A Survey of the Evolution of Language Model-Based Dialogue Systems
Dialogue systems, including task-oriented_dialogue_system (TOD) and
open-domain_dialogue_system (ODD), have undergone significant transformations,
with language_models (LM) playing a central role. This survey delves into the
historical trajectory of dialogue systems, elucidating their intricate
relationship with advancements in language models by categorizing this
evolution into four distinct stages, each marked by pivotal LM breakthroughs:
1) Early_Stage: characterized by statistical LMs, resulting in rule-based or
machine-learning-driven dialogue_systems; 2) Independent development of TOD and
ODD based on neural_language_models (NLM; e.g., LSTM and GRU), since NLMs lack
intrinsic knowledge in their parameters; 3) fusion between different types of
dialogue systems with the advert of pre-trained_language_models (PLMs),
starting from the fusion between four_sub-tasks_within_TOD, and then
TOD_with_ODD; and 4) current LLM-based_dialogue_system, wherein LLMs can be
used to conduct TOD and ODD seamlessly. Thus, our survey provides a
chronological perspective aligned with LM breakthroughs, offering a
comprehensive review of state-of-the-art research outcomes. What's more, we
focus on emerging topics and discuss open challenges, providing valuable
insights into future directions for LLM-based_dialogue_systems. Through this
exploration, we pave the way for a deeper_comprehension of the evolution,
guiding future developments in LM-based dialogue_systems
LitLLM: A Toolkit for Scientific Literature Review
Conducting literature reviews for scientific papers is essential for
understanding research, its limitations, and building on existing work. It is a
tedious task which makes an automatic literature review generator appealing.
Unfortunately, many existing works that generate such reviews using Large
Language Models (LLMs) have significant limitations. They tend to
hallucinate-generate non-actual information-and ignore the latest research they
have not been trained on. To address these limitations, we propose a toolkit
that operates on Retrieval Augmented Generation (RAG) principles, specialized
prompting and instructing techniques with the help of LLMs. Our system first
initiates a web search to retrieve relevant papers by summarizing user-provided
abstracts into keywords using an off-the-shelf LLM. Authors can enhance the
search by supplementing it with relevant papers or keywords, contributing to a
tailored retrieval process. Second, the system re-ranks the retrieved papers
based on the user-provided abstract. Finally, the related work section is
generated based on the re-ranked results and the abstract. There is a
substantial reduction in time and effort for literature review compared to
traditional methods, establishing our toolkit as an efficient alternative. Our
open-source toolkit is accessible at https://github.com/shubhamagarwal92/LitLLM
and Huggingface space (https://huggingface.co/spaces/shubhamagarwal92/LitLLM)
with the video demo at https://youtu.be/E2ggOZBAFw0
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