10 research outputs found
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
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
Structured Attention for Unsupervised Dialogue Structure Induction
Inducing a meaningful structural representation from one or a set of
dialogues is a crucial but challenging task in computational linguistics.
Advancement made in this area is critical for dialogue system design and
discourse analysis. It can also be extended to solve grammatical inference. In
this work, we propose to incorporate structured attention layers into a
Variational Recurrent Neural Network (VRNN) model with discrete latent states
to learn dialogue structure in an unsupervised fashion. Compared to a vanilla
VRNN, structured attention enables a model to focus on different parts of the
source sentence embeddings while enforcing a structural inductive bias.
Experiments show that on two-party dialogue datasets, VRNN with structured
attention learns semantic structures that are similar to templates used to
generate this dialogue corpus. While on multi-party dialogue datasets, our
model learns an interactive structure demonstrating its capability of
distinguishing speakers or addresses, automatically disentangling dialogues
without explicit human annotation.Comment: Long paper accepted by EMNLP 202
Discovering latent structure in task-oriented dialogues.
Abstract A key challenge for computational conversation models is to discover latent structure in task-oriented dialogue, since it provides a basis for analysing, evaluating, and building conversational systems. We propose three new unsupervised models to discover latent structures in task-oriented dialogues. Our methods synthesize hidden Markov models (for underlying state) and topic models (to connect words to states). We apply them to two real, non-trivial datasets: human-computer spoken dialogues in bus query service, and humanhuman text-based chats from a live technical support service. We show that our models extract meaningful state representations and dialogue structures consistent with human annotations. Quantitatively, we show our models achieve superior performance on held-out log likelihood evaluation and an ordering task
Harvesting and summarizing user-generated content for advanced speech-based human-computer interaction
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 155-164).There have been many assistant applications on mobile devices, which could help people obtain rich Web content such as user-generated data (e.g., reviews, posts, blogs, and tweets). However, online communities and social networks are expanding rapidly and it is impossible for people to browse and digest all the information via simple search interface. To help users obtain information more efficiently, both the interface for data access and the information representation need to be improved. An intuitive and personalized interface, such as a dialogue system, could be an ideal assistant, which engages a user in a continuous dialogue to garner the user's interest and capture the user's intent, and assists the user via speech-navigated interactions. In addition, there is a great need for a type of application that can harvest data from the Web, summarize the information in a concise manner, and present it in an aggregated yet natural way such as direct human dialogue. This thesis, therefore, aims to conduct research on a universal framework for developing speech-based interface that can aggregate user-generated Web content and present the summarized information via speech-based human-computer interaction. To accomplish this goal, several challenges must be met. Firstly, how to interpret users' intention from their spoken input correctly? Secondly, how to interpret the semantics and sentiment of user-generated data and aggregate them into structured yet concise summaries? Lastly, how to develop a dialogue modeling mechanism to handle discourse and present the highlighted information via natural language? This thesis explores plausible approaches to tackle these challenges. We will explore a lexicon modeling approach for semantic tagging to improve spoken language understanding and query interpretation. We will investigate a parse-and-paraphrase paradigm and a sentiment scoring mechanism for information extraction from unstructured user-generated data. We will also explore sentiment-involved dialogue modeling and corpus-based language generation approaches for dialogue and discourse. Multilingual prototype systems in multiple domains have been implemented for demonstration.by Jingjing Liu.Ph.D
Explainable Recommendation: Theory and Applications
Although personalized recommendation has been investigated for decades, the
wide adoption of Latent Factor Models (LFM) has made the explainability of
recommendations a critical issue to both the research community and practical
application of recommender systems. For example, in many practical systems the
algorithm just provides a personalized item recommendation list to the users,
without persuasive personalized explanation about why such an item is
recommended while another is not. Unexplainable recommendations introduce
negative effects to the trustworthiness of recommender systems, and thus affect
the effectiveness of recommendation engines. In this work, we investigate
explainable recommendation in aspects of data explainability, model
explainability, and result explainability, and the main contributions are as
follows:
1. Data Explainability: We propose Localized Matrix Factorization (LMF)
framework based Bordered Block Diagonal Form (BBDF) matrices, and further
applied this technique for parallelized matrix factorization.
2. Model Explainability: We propose Explicit Factor Models (EFM) based on
phrase-level sentiment analysis, as well as dynamic user preference modeling
based on time series analysis. In this work, we extract product features and
user opinions towards different features from large-scale user textual reviews
based on phrase-level sentiment analysis techniques, and introduce the EFM
approach for explainable model learning and recommendation.
3. Economic Explainability: We propose the Total Surplus Maximization (TSM)
framework for personalized recommendation, as well as the model specification
in different types of online applications. Based on basic economic concepts, we
provide the definitions of utility, cost, and surplus in the application
scenario of Web services, and propose the general framework of web total
surplus calculation and maximization.Comment: 169 pages, in Chinese, 3 main research chapter