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์ง์ ๊ธฐ๋ฐ ๋ํ์์์ ๋ํ ํน์ฑ์ ํ์ฉํ ์ง์ ์ ํ ๋ฐ ๋ญํน ๋ฐฉ๋ฒ
ํ์๋
ผ๋ฌธ(๋ฐ์ฌ) -- ์์ธ๋ํ๊ต๋ํ์ : ๊ณต๊ณผ๋ํ ์ ๊ธฐยท์ปดํจํฐ๊ณตํ๋ถ, 2022. 8. ์ด์๊ตฌ.Knowledge grounded conversation (KGC) model aims to generate informative responses relevant to both conversation history and external knowledge. One of the most important parts of KGC models is to find the knowledge which provides the basis on which the responses are grounded. If the model selects inappropriate knowledge, it may produce responses that are irrelevant or lack knowledge. In this dissertation, we study the methods of leveraging conversational characteristics to select or rank the knowledge for knowledge grounded conversation.
In particular, this dissertation provides novel two methods, where one of which focuses on the sequential structure of multi-turn conversation, and the other focuses on utilizing local context and topic of a long conversation. We first propose two knowledge selection strategies of which one preserves the sequential matching features and the other encodes the sequential nature of the conversation. Second, we propose a novel knowledge ranking model that composes an appropriate range of relevant documents by exploiting both the topic keywords and local context of a conversation. In addition, we apply the knowledge ranking model in quote recommendation with our new quote recommendation framework that provides hard negative samples to the model. Our experimental results show that the KGC models based on our proposed knowledge selection and ranking methods outperform the competitive models in terms of groundness and relevance.์ง์ ๊ธฐ๋ฐ ๋ํ ๋ชจ๋ธ์ ๋ํ ๊ธฐ๋ก๊ณผ ์ธ๋ถ ์ง์ ์ด ๋ ๊ฐ์ง ๋ชจ๋์ ๊ด๋ จ๋ ์๋ต์ ์์ฑํ๋ ๊ฒ์ ๋ชฉํ๋ก ํ๋ค. ์ง์ ๊ธฐ๋ฐ ๋ํ ๋ชจ๋ธ์ ๊ฐ์ฅ ์ค์ํ ๋ถ๋ถ ์ค ํ๋๋ ์๋ต์ ๊ธฐ๋ฐ์ ์ ๊ณตํ๋ ์ง์์ ์ฐพ๋ ๊ฒ์ด๋ค. ์ง์ ๊ธฐ๋ฐ ๋ชจ๋ธ์ด ์ฃผ์ด์ง ๋ฌธ๋งฅ์ ๋ถ์ ํฉํ ์ง์์ ์ฐพ๋ ๊ฒฝ์ฐ ๊ด๋ จ์ฑ์ด ๋จ์ด์ง๊ฑฐ๋ ์ง์์ด ๋ถ์กฑํ ์๋ต์ด ์์ฑ๋ ์ ์๋ค. ์ด ๋ฌธ์ ๋ฅผ ํด๊ฒฐํ๊ธฐ ์ํด ์ด ๋
ผ๋ฌธ์์๋ ์ง์ ๊ธฐ๋ฐ ๋ํ๋ฅผ ์ํด ๋ํ ์ฌ๋ฌ ํน์ฑ์ ํ์ฉํ์ฌ ์ง์์ ์ ์ ํ๋ ์ง์ ์ ํ ๋ชจ๋ธ๊ณผ ์ง์ ์์ ๋ชจ๋ธ์ ์ ์ํ๋ค.
๊ตฌ์ฒด์ ์ผ๋ก ๋ณธ ๋
ผ๋ฌธ์์๋ ๋ค์ค ํด ๋ํ์์์ ์์ฐจ์ ๊ตฌ์กฐ ๋๋ ์๋ต ์ด์ ๋ฌธ๋งฅ๊ณผ ๋ํ์ ์ฃผ์ ๋ฅผ ํ์ฉํ๋ ์๋ก์ด ๋ ๊ฐ์ง ๋ฐฉ๋ฒ์ ์ ์ํ๋ค. ์ฒซ ๋ฒ์งธ ๋ฐฉ๋ฒ์ผ๋ก์จ ๋ณธ ๋
ผ๋ฌธ์ ๋ ๊ฐ์ง ์ง์ ์ ํ ์ ๋ต์ ์ ์ํ๋ค. ์ ์ํ๋ ์ ๋ต ์ค ํ๋๋ ์ง์๊ณผ ๋ํ ํด ๊ฐ์ ์์ฐจ์ ๋งค์นญ ํน์ง์ ๋ณด์กดํ๋ ๋ฐฉ๋ฒ์ด๊ณ ๋ค๋ฅธ ์ ๋ต์ ๋ํ์ ์์ฐจ์ ํน์ฑ์ ์ธ์ฝ๋ฉํ์ฌ ์ง์์ ์ ํํ๋ ๋ฐฉ๋ฒ์ด๋ค. ๋ ๋ฒ์งธ๋ก ๋ณธ ๋
ผ๋ฌธ์ ๋ํ์ ์ฃผ์ ํค์๋์ ์๋ต ๋ฐ๋ก ์ด์ ์ ๋ฌธ๋งฅ์ ๋ชจ๋ ํ์ฉํ์ฌ ์ ์ ํ ๋ฒ์์ ๊ด๋ จ ๋ฌธ์๋ค๋ก ๊ฒ์ ๊ฒฐ๊ณผ๋ฅผ ๊ตฌ์ฑํ๋ ์๋ก์ด ์ง์ ์์ ๋ชจ๋ธ์ ์ ์ํ๋ค. ๋ง์ง๋ง์ผ๋ก ์ง์ ์์ ๋ชจ๋ธ์ ์ ์์ฑ ๊ฒ์ฆ์ ์ํด ์ ๋ต ์ธ์ฉ๊ตฌ์ ์๋ฏธ์ ์ผ๋ก ์ ์ฌํ์ง๋ง ์ ๋ต์ ์๋ ์ธ์ฉ๊ตฌ์ ์งํฉ์ ์ธ์ฉ๊ตฌ ์์ ๋ชจ๋ธ์ ์ ๊ณตํ๋ ์ธ์ฉ๊ตฌ ์ถ์ฒ ํ๋ ์์ํฌ๋ฅผ ์ ์ํ๋ค. ์ ์๋ ์ง์ ์ ํ ๋ฐ ์์ ๋ชจ๋ธ์ ๊ธฐ๋ฐ์ผ๋ก ํ๋ ์ง์ ๊ธฐ๋ฐ ๋ํ ๋ชจ๋ธ์ด ๊ฒฝ์ ๋ชจ๋ธ๋ณด๋ค ์ธ๋ถ ์ง์ ๋ฐ ๋ํ ๋ฌธ๋งฅ๊ณผ์ ๊ด๋ จ์ฑ ์ธก๋ฉด์์ ์ฐ์ํ๋ค๋ ๊ฒ์ ์ฌ๋ ๊ฐ์ ๋ํ ๋ฐ์ดํฐ๋ฅผ ์ด์ฉํ ๋ค์์ ์คํ์ ํตํด ๊ฒ์ฆํ์๋ค.Abstract 1
1. Introduction 17
2. Background and Related Works 25
2.1 Terminology 25
2.2 Overview of Technologies for Conversational Systems 27
2.2.1 Open-domain Dialogue System 27
2.2.2 Task-oriented Dialogue System 29
2.2.3 Question Answering System 29
2.3 Components of Knowledge Grounded Conversation Model 31
2.4 Related Works 36
2.4.1 KGC datasets 36
2.4.2 Soft Selection-based KGC Model 36
2.4.3 Hard Selection-based KGC Model 37
2.4.4 Retrieval-based KGC Models 39
2.4.5 Response Generation with Knowledge Integration 39
2.4.6 Quote Recommendation 42
2.5 Evaluation Methods 44
2.6 Problem Statements 47
3. Knowledge Selection with Sequential Structure of Conversation 48
3.1 Motivation 48
3.2 Reduce-Match Strategy & Match-Reduce Strategy 49
3.2.1 Backbone architecture 51
3.2.2 Reduce-Match Strategy-based Models 52
3.2.3 Match-Reduce Strategy-based Models 56
3.3 Experiments 62
3.3.1 Experimental Setup 62
3.3.2 Experimental Results 70
3.4 Analysis 72
3.4.1 Case Study 72
3.4.2 Impact of Matching Difficulty 75
3.4.3 Impact of Length of Context 77
3.4.4 Impact of Dialogue Act of Message 78
4. Knowledge Ranking with Local Context and Topic Keywords 81
4.1 Motivation 81
4.2 Retrieval-Augmented Knowledge Grounded Conversation Model 85
4.2.1 Base Model 86
4.2.2 Topic-aware Dual Matching for Knowledge Re-ranking 86
4.2.3 Data Weighting Scheme for Retrieval Augmented Generation Models 89
4.3 Experiments 90
4.3.1 Experimental Setup 90
4.3.2 Experimental Results 94
4.4 Analysis 98
4.4.1 Case Study 98
4.4.2 Ablation Study 99
4.4.3 Model Variations 104
4.4.4 Error Analysis 105
5. Application: Quote Recommendation with Knowledge Ranking 110
5.1 Motivation 110
5.2 CAGAR: A Framework for Quote Recommendation 112
5.2.1 Conversation Encoder 114
5.2.2 Quote Encoder 114
5.2.3 Candidate Generator 115
5.2.4 Re-ranker 116
5.2.5 Training and Inference 116
5.3 Experiments 117
5.3.1 Experimental Setup 117
5.3.2 Experimental Results 119
5.4 Analysis 120
5.4.1 Ablation Study 120
5.4.2 Case Study 121
5.4.3 Impact of Length of Context 121
5.4.4 Impact of Training Set Size per Quote 123
6. Conclusion 125
6.1 Contributions and Limitations 126
6.2 Future Works 128
Appendix A. Preliminary Experiments for Quote Recommendations 131
A.1 Methods 131
A.1.1 Matching Granularity Adjustment 131
A.1.2 Random Forest 133
A.1.3 Convolutional Neural Network 133
A.1.4 Recurrent Neural Network 134
A.2 Experiments 135
A.2.1 Baselines and Implementation Details 135
A.2.2 Datasets 136
A.2.3 Results and Discussions 137
์ด๋ก 162๋ฐ
News Session-Based Recommendations using Deep Neural Networks
News recommender systems are aimed to personalize users experiences and help
them to discover relevant articles from a large and dynamic search space.
Therefore, news domain is a challenging scenario for recommendations, due to
its sparse user profiling, fast growing number of items, accelerated item's
value decay, and users preferences dynamic shift. Some promising results have
been recently achieved by the usage of Deep Learning techniques on Recommender
Systems, specially for item's feature extraction and for session-based
recommendations with Recurrent Neural Networks. In this paper, it is proposed
an instantiation of the CHAMELEON -- a Deep Learning Meta-Architecture for News
Recommender Systems. This architecture is composed of two modules, the first
responsible to learn news articles representations, based on their text and
metadata, and the second module aimed to provide session-based recommendations
using Recurrent Neural Networks. The recommendation task addressed in this work
is next-item prediction for users sessions: "what is the next most likely
article a user might read in a session?" Users sessions context is leveraged by
the architecture to provide additional information in such extreme cold-start
scenario of news recommendation. Users' behavior and item features are both
merged in an hybrid recommendation approach. A temporal offline evaluation
method is also proposed as a complementary contribution, for a more realistic
evaluation of such task, considering dynamic factors that affect global
readership interests like popularity, recency, and seasonality. Experiments
with an extensive number of session-based recommendation methods were performed
and the proposed instantiation of CHAMELEON meta-architecture obtained a
significant relative improvement in top-n accuracy and ranking metrics (10% on
Hit Rate and 13% on MRR) over the best benchmark methods.Comment: Accepted for the Third Workshop on Deep Learning for Recommender
Systems - DLRS 2018, October 02-07, 2018, Vancouver, Canada.
https://recsys.acm.org/recsys18/dlrs
Context-Based Quotation Recommendation
While composing a new document, anything from a news article to an email or
essay, authors often utilize direct quotes from a variety of sources. Although
an author may know what point they would like to make, selecting an appropriate
quote for the specific context may be time-consuming and difficult. We
therefore propose a novel context-aware quote recommendation system which
utilizes the content an author has already written to generate a ranked list of
quotable paragraphs and spans of tokens from a given source document.
We approach quote recommendation as a variant of open-domain question
answering and adapt the state-of-the-art BERT-based methods from open-QA to our
task. We conduct experiments on a collection of speech transcripts and
associated news articles, evaluating models' paragraph ranking and span
prediction performances. Our experiments confirm the strong performance of
BERT-based methods on this task, which outperform bag-of-words and neural
ranking baselines by more than 30% relative across all ranking metrics.
Qualitative analyses show the difficulty of the paragraph and span
recommendation tasks and confirm the quotability of the best BERT model's
predictions, even if they are not the true selected quotes from the original
news articles.Comment: 12 pages, 3 figure
Ripple Knowledge Graph Convolutional Networks For Recommendation Systems
Using knowledge graphs to assist deep learning models in making
recommendation decisions has recently been proven to effectively improve the
model's interpretability and accuracy. This paper introduces an end-to-end deep
learning model, named RKGCN, which dynamically analyses each user's preferences
and makes a recommendation of suitable items. It combines knowledge graphs on
both the item side and user side to enrich their representations to maximize
the utilization of the abundant information in knowledge graphs. RKGCN is able
to offer more personalized and relevant recommendations in three different
scenarios. The experimental results show the superior effectiveness of our
model over 5 baseline models on three real-world datasets including movies,
books, and music
Personalized Memory Transfer for Conversational Recommendation Systems
Dialogue systems are becoming an increasingly common part of many users\u27 daily routines. Natural language serves as a convenient interface to express our preferences with the underlying systems. In this work, we implement a full-fledged Conversational Recommendation System, mainly focusing on learning user preferences through online conversations. Compared to the traditional collaborative filtering setting where feedback is provided quantitatively, conversational users may only indicate their preferences at a high level with inexact item mentions in the form of natural language chit-chat. This makes it harder for the system to correctly interpret user intent and in turn provide useful recommendations to the user. To tackle the ambiguities in natural language conversations, we propose Personalized Memory Transfer (PMT) which learns a personalized model in an online manner by leveraging a key-value memory structure to distill user feedback directly from conversations. This memory structure enables the integration of prior knowledge to transfer existing item representations/preferences and natural language representations. We also implement a retrieval based response generation module, where the system in addition to recommending items to the user, also responds to the user, either to elicit more information regarding the user intent or just for a casual chit-chat. The experiments were conducted on two public datasets and the results demonstrate the effectiveness of the proposed approach
Classification-based Deep Neural Network Architecture for Collaborative Filtering Recommender Systems
This paper proposes a scalable and original classification-based deep neural architecture. Its collaborative filtering approach can be generalized to most of the existing recommender systems, since it just operates on the ratings dataset. The learning process is based on the binary relevant/non-relevant vote and the binary voted/non-voted item information. This data reduction provides a new level of abstraction and it makes possible to design the classification-based architecture. In addition to the original architecture, its prediction process has a novel approach: it does not need to make a large number of predictions to get recommendations. Instead to run forward the neural network for each prediction, our approach runs forward the neural network just once to get a set of probabilities in its categorical output layer. The proposed neural architecture has been tested by using the MovieLens and FilmTrust datasets. A state-of-the-art baseline that outperforms current competitive approaches has been used. Results show a competitive recommendation quality and an interesting quality improvement on large number of recommendations, consistent with the architecture design. The architecture originality makes it possible to address a broad range of future works
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