Best-Answer Prediction in Q&A Sites Using User Information
- URL: http://arxiv.org/abs/2212.08475v1
- Date: Thu, 15 Dec 2022 02:28:52 GMT
- Title: Best-Answer Prediction in Q&A Sites Using User Information
- Authors: Rafik Hadfi, Ahmed Moustafa, Kai Yoshino, Takayuki Ito
- Abstract summary: Community Question Answering (CQA) sites have spread and multiplied significantly in recent years.
One practical way of finding such answers is automatically predicting the best candidate given existing answers and comments.
We address this limitation using a novel method for predicting the best answers using the questioner's background information and other features.
- Score: 2.982218441172364
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Community Question Answering (CQA) sites have spread and multiplied
significantly in recent years. Sites like Reddit, Quora, and Stack Exchange are
becoming popular amongst people interested in finding answers to diverse
questions. One practical way of finding such answers is automatically
predicting the best candidate given existing answers and comments. Many studies
were conducted on answer prediction in CQA but with limited focus on using the
background information of the questionnaires. We address this limitation using
a novel method for predicting the best answers using the questioner's
background information and other features, such as the textual content or the
relationships with other participants. Our answer classification model was
trained using the Stack Exchange dataset and validated using the Area Under the
Curve (AUC) metric. The experimental results show that the proposed method
complements previous methods by pointing out the importance of the
relationships between users, particularly throughout the level of involvement
in different communities on Stack Exchange. Furthermore, we point out that
there is little overlap between user-relation information and the information
represented by the shallow text features and the meta-features, such as time
differences.
Related papers
- Feature Engineering in Learning-to-Rank for Community Question Answering
Task [2.5091819952713057]
Community question answering (CQA) forums are Internet-based platforms where users ask questions about a topic and other expert users try to provide solutions.
Many CQA forums such as Quora, Stackoverflow, Yahoo!Answer, StackExchange exist with a lot of user-generated data.
These data are leveraged in automated CQA ranking systems where similar questions (and answers) are presented in response to the query of the user.
arXiv Detail & Related papers (2023-09-14T11:18:26Z) - Answering Ambiguous Questions with a Database of Questions, Answers, and
Revisions [95.92276099234344]
We present a new state-of-the-art for answering ambiguous questions that exploits a database of unambiguous questions generated from Wikipedia.
Our method improves performance by 15% on recall measures and 10% on measures which evaluate disambiguating questions from predicted outputs.
arXiv Detail & Related papers (2023-08-16T20:23:16Z) - CREPE: Open-Domain Question Answering with False Presuppositions [92.20501870319765]
We introduce CREPE, a QA dataset containing a natural distribution of presupposition failures from online information-seeking forums.
We find that 25% of questions contain false presuppositions, and provide annotations for these presuppositions and their corrections.
We show that adaptations of existing open-domain QA models can find presuppositions moderately well, but struggle when predicting whether a presupposition is factually correct.
arXiv Detail & Related papers (2022-11-30T18:54:49Z) - Answer ranking in Community Question Answering: a deep learning approach [0.0]
This work tries to advance the state of the art on answer ranking for community Question Answering by proceeding with a deep learning approach.
We created a large data set of questions and answers posted to the Stack Overflow website.
We leveraged the natural language processing capabilities of dense embeddings and LSTM networks to produce a prediction for the accepted answer attribute.
arXiv Detail & Related papers (2022-10-16T18:47:41Z) - Multifaceted Improvements for Conversational Open-Domain Question
Answering [54.913313912927045]
We propose a framework with Multifaceted Improvements for Conversational open-domain Question Answering (MICQA)
Firstly, the proposed KL-divergence based regularization is able to lead to a better question understanding for retrieval and answer reading.
Second, the added post-ranker module can push more relevant passages to the top placements and be selected for reader with a two-aspect constrains.
Third, the well designed curriculum learning strategy effectively narrows the gap between the golden passage settings of training and inference, and encourages the reader to find true answer without the golden passage assistance.
arXiv Detail & Related papers (2022-04-01T07:54:27Z) - HeteroQA: Learning towards Question-and-Answering through Multiple
Information Sources via Heterogeneous Graph Modeling [50.39787601462344]
Community Question Answering (CQA) is a well-defined task that can be used in many scenarios, such as E-Commerce and online user community for special interests.
Most of the CQA methods only incorporate articles or Wikipedia to extract knowledge and answer the user's question.
We propose a question-aware heterogeneous graph transformer to incorporate the multiple information sources (MIS) in the user community to automatically generate the answer.
arXiv Detail & Related papers (2021-12-27T10:16:43Z) - A Graph-guided Multi-round Retrieval Method for Conversational
Open-domain Question Answering [52.041815783025186]
We propose a novel graph-guided retrieval method to model the relations among answers across conversation turns.
We also propose to incorporate the multi-round relevance feedback technique to explore the impact of the retrieval context on current question understanding.
arXiv Detail & Related papers (2021-04-17T04:39:41Z) - Attention-based model for predicting question relatedness on Stack
Overflow [0.0]
We propose an Attention-based Sentence pair Interaction Model (ASIM) to predict the relatedness between questions on Stack Overflow automatically.
ASIM has made significant improvement over the baseline approaches in Precision, Recall, and Micro-F1 evaluation metrics.
Our model also performs well in the duplicate question detection task of Ask Ubuntu.
arXiv Detail & Related papers (2021-03-19T12:18:03Z) - Features that Predict the Acceptability of Java and JavaScript Answers
on Stack Overflow [5.332217496693262]
We studied the Stack Overflow dataset by analyzing questions and answers for the two most popular tags (Java and JavaScript)
Our findings reveal that the length of code in answers, reputation of users, similarity of the text between questions and answers, and the time lag between questions and answers have the highest predictive power for differentiating accepted and unaccepted answers.
arXiv Detail & Related papers (2021-01-08T03:09:38Z) - Diverse and Non-redundant Answer Set Extraction on Community QA based on
DPPs [18.013010857062643]
In community-based question answering platforms, it takes time for a user to get useful information from among many answers.
This paper proposes a new task of selecting a diverse and non-redundant answer set rather than ranking the answers.
arXiv Detail & Related papers (2020-11-18T07:33:03Z) - Visual Question Answering with Prior Class Semantics [50.845003775809836]
We show how to exploit additional information pertaining to the semantics of candidate answers.
We extend the answer prediction process with a regression objective in a semantic space.
Our method brings improvements in consistency and accuracy over a range of question types.
arXiv Detail & Related papers (2020-05-04T02:46:31Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.