Question Retrieval Research Articles

Can survey design reduce anchoring bias in recall data? Evidence from smallholder farmers in Malawi

Abstract Recall biases in retrospective self-reported survey data have important implications for empirical research. We leverage the survey design literature and test three strategies to attenuate mental anchoring in retrospective data collection: question ordering, retrieval cues and aggregate (community) anchoring. We focus on maize production and happiness reports among smallholder farmers in Malawi. Asking for retrospective before concurrent data on average reduces recall bias (i.e. the deviation of the recalled versus the concurrent outcome reported in the previous period) by 34 per cent for maize production, a meaningful improvement with no increase in data collection costs. Retrieval cues are less successful and community anchors can exacerbate the bias. None of the strategies help to ease the recall bias for happiness reports.

HỌC BIỂU DIỄN CÂU SỬ DỤNG MÔ HÌNH LSTM TRONG BÀI TOÁN TÌM KIẾM CÂU HỎI

Learning sentence representation with the full semantics of a document is a challenge in natural language processing problems because if the semantic representation vector of the sentence is suitable, it will increase the performance of finding similar question problems. In this paper, we propose implementing a series of LSTM models with different ways of extracting sentence representations and applying them to question retrieval to exploit the hidden semantics of sentences. These methods give sentence representation from hidden layers of the LSTM model. The results show that the technique using a combination of both Max Pooling and Mean Pooling gives the highest results on the 2017 SemEval dataset for the problem of finding similarity questions. Keywords: LSTM; Deep Learning; NLP; QA; learning sentence representation; CQA.

Knowledge Graph Prompting for Multi-Document Question Answering

The `pre-train, prompt, predict' paradigm of large language models (LLMs) has achieved remarkable success in open-domain question answering (OD-QA). However, few works explore this paradigm in multi-document question answering (MD-QA), a task demanding a thorough understanding of the logical associations among the contents and structures of documents. To fill this crucial gap, we propose a Knowledge Graph Prompting (KGP) method to formulate the right context in prompting LLMs for MD-QA, which consists of a graph construction module and a graph traversal module. For graph construction, we create a knowledge graph (KG) over multiple documents with nodes symbolizing passages or document structures (e.g., pages/tables), and edges denoting the semantic/lexical similarity between passages or document structural relations. For graph traversal, we design an LLM-based graph traversal agent that navigates across nodes and gathers supporting passages assisting LLMs in MD-QA. The constructed graph serves as the global ruler that regulates the transitional space among passages and reduces retrieval latency. Concurrently, the graph traversal agent acts as a local navigator that gathers pertinent context to progressively approach the question and guarantee retrieval quality. Extensive experiments underscore the efficacy of KGP for MD-QA, signifying the potential of leveraging graphs in enhancing the prompt design and retrieval augmented generation for LLMs. Our code: https://github.com/YuWVandy/KG-LLM-MDQA.

Harnessing the Power of Metadata for Enhanced Question Retrieval in Community Question Answering

Harnessing the Power of Metadata for Enhanced Question Retrieval in Community Question Answering

Similar question retrieval with incorporation of multi-dimensional quality analysis for community question answering

Similar question retrieval with incorporation of multi-dimensional quality analysis for community question answering

A systematic review of similar Questions Retrieval Approaches

A systematic review of similar Questions Retrieval Approaches

Analysis of Communication Effectiveness in Motivating Employees (Case Study at PT AMG Plastik Industri)

PT Industri Plastik AMG (AMG Plastic) has departments of the company whose job is to carry out its workflow. In the task of preparing HR needs so that they have the qualifications in accordance with what the company is looking for, it cannot be separated from the role of employees in the Human Relations Division (HRD). The employees in this division are an important part, in which to increase the motivation of these employees must be fully supported by the role of the division head at PT Industri Plastik AMG (AMG Plastic) in creating a good internal company relationship and the relationship between superiors and subordinates. The research method approach used in this study is a qualitative research method by adapting theories related to organizational communication and employee motivation theories through observation, question and answer and retrieval of documentation related to how communication activities are carried out by superiors towards employees to provide constructive motivation to improve performance and work commitment at PT Industri Plastik AMG (AMG Plastic)

An Intelligent Conversational Agent for the Legal Domain

An intelligent conversational agent for the legal domain is an AI-powered system that can communicate with users in natural language and provide legal advice or assistance. In this paper, we present CREA2, an agent designed to process legal concepts and be able to guide users on legal matters. The conversational agent can help users navigate legal procedures, understand legal jargon, and provide recommendations for legal action. The agent can also give suggestions helpful in drafting legal documents, such as contracts, leases, and notices. Additionally, conversational agents can help reduce the workload of legal professionals by handling routine legal tasks. CREA2, in particular, will guide the user in resolving disputes between people residing within the European Union, proposing solutions in controversies between two or more people who are contending over assets in a divorce, an inheritance, or the division of a company. The conversational agent can later be accessed through various channels, including messaging platforms, websites, and mobile applications. This paper presents a retrieval system that evaluates the similarity between a user’s query and a given question. The system uses natural language processing (NLP) algorithms to interpret user input and associate responses by addressing the problem as a semantic search similar question retrieval. Although a common approach to question and answer (Q&A) retrieval is to create labelled Q&A pairs for training, we exploit an unsupervised information retrieval system in order to evaluate the similarity degree between a given query and a set of questions contained in the knowledge base. We used the recently proposed SBERT model for the evaluation of relevance. In the paper, we illustrate the effective design principles, the implemented details and the results of the conversational system and describe the experimental campaign carried out on it.

I Know What You Are Searching for: Code Snippet Recommendation from Stack Overflow Posts

Stack Overflow has been heavily used by software developers to seek programming-related information. More and more developers use Community Question and Answer forums, such as Stack Overflow, to search for code examples of how to accomplish a certain coding task. This is often considered to be more efficient than working from source documentation, tutorials, or full worked examples. However, due to the complexity of these online Question and Answer forums and the very large volume of information they contain, developers can be overwhelmed by the sheer volume of available information. This makes it hard to find and/or even be aware of the most relevant code examples to meet their needs. To alleviate this issue, in this work, we present a query-driven code recommendation tool, named Que2Code , that identifies the best code snippets for a user query from Stack Overflow posts. Our approach has two main stages: (i) semantically equivalent question retrieval and (ii) best code snippet recommendation. During the first stage, for a given query question formulated by a developer, we first generate paraphrase questions for the input query as a way of query boosting and then retrieve the relevant Stack Overflow posted questions based on these generated questions. In the second stage, we collect all of the code snippets within questions retrieved in the first stage and develop a novel scheme to rank code snippet candidates from Stack Overflow posts via pairwise comparisons. To evaluate the performance of our proposed model, we conduct a large-scale experiment to evaluate the effectiveness of the semantically equivalent question retrieval task and best code snippet recommendation task separately on Python and Java datasets in Stack Overflow. We also perform a human study to measure how real-world developers perceive the results generated by our model. Both the automatic and human evaluation results demonstrate the promising performance of our model, and we have released our code and data to assist other researchers.

Research on question retrieval method for community question answering

Research on question retrieval method for community question answering

ARNN-QA: Adaptive Recurrent Neural Network with feature optimization for incremental learning-based Question Answering system

The evolution of internet services has raised many information retrieval systems, and the use of intelligent services for the Question Answering (QA) systems has been on ascending. QA system identifies the accurate answers briefly, and the answers are identified through natural language expressions. Developing intelligent techniques for QA has been an abiding issue, and it has been studied for over years. Though, different QA models focus on common-sense and general questions in open fields, which are incompetent in solving the more complex professional questions. Moreover, QA models are the most emerging area in computer science nowadays. It is more significant as it includes more deep learning derived questions. However, the existing models suffer from question retrieval under the incremental learning system. Hence, this paper introduces the QA system based on feature extraction, feature optimization, and learning. Initially, the text input is subjected to feature extraction, in which word to vector is used. Further, optimal feature selection is employed using the Sail Fish-based Whale Optimization Algorithm (SF-WOA). Once the feature optimization is done, the question-answering learning is developed by the Adaptive Recurrent Neural Network (A-RNN). As this is the incremental learning system, the proposed architecture adaptively adjusts the weight for the new incoming question by the proposed SF-WOA, so that it can learn new questions other than the trained data. The performance of the suggested SF-WOA-RNN in terms of remembrance is 8.2%, 6.9%, 4.5%, and 4.5% progressed than GWO-RNN WOA-RNN, PSO-RNN, and SFO-RNN, respectively while considering test case 1. Finally, the experiments are tested with a set of open-source datasets and prove that the suggested model is effective and holds better answers than the traditional models.

Efficient Data Mining Model for Question Retrieval and Question Analytics using Semantic Web Framework in Smart E-Learning Environment

In the field of Information recovery, the fundamental target is to discover important just as most applicable data concerning a few questions. However, the essential issue regarding recuperation has reliably been, that the request for an area is enormous so much that it has gotten very difficult to recuperate applicable information capably. In any case, with the latest progressions in profound learning and AI models, calculations, applications brilliant and computerized data recovery component matched with text examination to decide different characterizing boundaries alongside intricacy and weight-age assurance of inquiries. By focusing, the cutoff points and hardships, like CPU cost, efficiency, automation and congruity, we have assigned our information recuperation structure, particularly towards the Academic Institutional Domain to consider the interest of various association related inquiries. The aim is to make an efficient data mining and an analytical model that can automate an efficient question retrieval and analysis for complexity and weight-age determination.

Adversarial Cross-domain Community Question Retrieval

Community Q&A forum is a special type of social media that provides a platform to raise questions and to answer them (both by forum participants), to facilitate online information sharing. Currently, community Q&A forums in professional domains have attracted a large number of users by offering professional knowledge. To support information access and save users’ efforts of raising new questions, they usually come with a question retrieval function, which retrieves similar existing questions (and their answers) to a user’s query. However, it can be difficult for community Q&A forums to cover all domains, especially those emerging lately with little labeled data but great discrepancy from existing domains. We refer to this scenario as cross-domain question retrieval. To handle the unique challenges of cross-domain question retrieval, we design a model based on adversarial training, namely, X-QR , which consists of two modules—a domain discriminator and a sentence matcher. The domain discriminator aims at aligning the source and target data distributions and unifying the feature space by domain-adversarial training. With the assistance of the domain discriminator, the sentence matcher is able to learn domain-consistent knowledge for the final matching prediction. To the best of our knowledge, this work is among the first to investigate the domain adaption problem of sentence matching for community Q&A forums question retrieval. The experiment results suggest that the proposed X-QR model offers better performance than conventional sentence matching methods in accomplishing cross-domain community Q&A tasks.

Learning English and Arabic question similarity with Siamese Neural Networks in community question answering services

In this paper, we tackle the task of similar question retrieval (QR) which is essential for Community Question Answering (cQA) and aims to retrieve historical questions that are semantically equivalent to the new queries. Over time, with the sharp increase of community archives and the accumulation of duplicated questions, the QR problem has become increasingly challenging due to the shortness of the community questions as well as the word mismatch problem as users can formulate the same query using different wording. Although many efforts have been devoted to address this problem, existing methods mostly relied on supervised models which significantly depend on massive training data sets and manual feature engineering. Such methods are chiefly constrained by their specificities that ignore the word order and do not capture enough syntactic and semantic information in questions. In this paper, we rely on Neural Networks (NNs) which use a deep analysis of words and questions to take into consideration the semantics as well as the structure of questions to predict the semantic text similarity. We propose a deep learning approach based on a Siamese architecture with Long Short-Term Memory (LSTM) networks, augmented with an attention mechanism to let the model give different words different attention while modeling questions. We also explore the use of Convolutional Neural Networks (CNN) nested within the Siamese architecture to retrieve relevant questions. Different similarity measures were tested to predict the semantic similarity between the pairs of questions. To evaluate the proposed approach, we conducted experiments on large-scale datasets in English and Arabic.

A study of similar question retrieval method in online health communities

Purpose The purpose of this paper is to propose a new approach to retrieve similar questions in online health communities to improve the efficiency of health information retrieval and sharing. Design/methodology/approach This paper proposes a hybrid approach to combining domain knowledge similarity and topic similarity to retrieve similar questions in online health communities. The domain knowledge similarity can evaluate the domain distance between different questions. And the topic similarity measures questions’ relationship base on the extracted latent topics. Findings The experiment results show that the proposed method outperforms the baseline methods. Originality/value This method conquers the problem of word mismatch and considers the named entities included in questions, which most of existing studies did not.

Chatbot4QR: Interactive Query Refinement for Technical Question Retrieval

Technical Q&#x0026;A sites (e.g., Stack Overflow (SO)) are important resources for developers to search for knowledge about technical problems. Search engines provided in Q&#x0026;A sites and information retrieval approaches (e.g., word embedding-based) have limited capabilities to retrieve relevant questions when queries are imprecisely specified, such as missing important technical details (e.g., the user&#x2019;s preferred programming languages). Although many automatic query expansion approaches have been proposed to improve the quality of queries by expanding queries with relevant terms, the information missed in a query is not identified. Moreover, without user involvement, the existing query expansion approaches may introduce unexpected terms and lead to undesired results. In this paper, we propose an interactive query refinement approach for question retrieval, named <i>Chatbot4QR</i>, which can assist users in recognizing and clarifying technical details missed in queries and thus retrieve more relevant questions for users. Chatbot4QR automatically detects missing technical details in a query and generates several clarification questions (CQs) to interact with the user to capture their overlooked technical details. To ensure the accuracy of CQs, we design a heuristic-based approach for CQ generation after building two kinds of technical knowledge bases: a manually categorized result of 1,841 technical tags in SO and the multiple version-frequency information of the tags. We develop a Chatbot4QR prototype that uses 1.88 million SO questions as the repository for question retrieval. To evaluate Chatbot4QR, we conduct six user studies with 25 participants on 50 experimental queries. The results are as follows. (1) On average 60.8 percent of the CQs generated for a query are useful for helping the participants recognize missing technical details. (2) Chatbot4QR can rapidly respond to the participants after receiving a query within approximately 1.3 seconds. (3) The refined queries contribute to retrieving more relevant SO questions than nine baseline approaches. For more than 70 percent of the participants who have preferred techniques on the query tasks, Chatbot4QR significantly outperforms the state-of-the-art word embedding-based retrieval approach with an improvement of at least 54.6 percent in terms of two measurements: Pre<inline-formula><tex-math notation="LaTeX">$@$</tex-math><alternatives><mml:math><mml:mo>@</mml:mo></mml:math><inline-graphic xlink:href="xia-ieq1-3016006.gif"/></alternatives></inline-formula>k and NDCG<inline-formula><tex-math notation="LaTeX">$@$</tex-math><alternatives><mml:math><mml:mo>@</mml:mo></mml:math><inline-graphic xlink:href="xia-ieq2-3016006.gif"/></alternatives></inline-formula>k. (4) For 48-88 percent of the assigned query tasks, the participants obtain more desired results after interacting with Chatbot4QR than directly searching from Web search engines (e.g., the SO search engine and Google) using the original queries.

Cross-lingual embedding for cross-lingual question retrieval in low-resource community question answering

In today’s digital world people are keen on finding the knowledge they need by surfing the internet to find the answers to their questions. To this aim, many Community Question Answering (CQA) systems are established, in which people can ask their question and receive the required information. The gathered data in such systems is a rich repository for people to search through the available questions that have been answered before. CQA users, however, are not always successful in finding their answers in their native CQA systems. One solution to enrich the searching process is translating input questions and searching them in other CQA systems. This solution is useless as the process of translating each question is time-consuming. To make the non-English CQA systems richer in finding the available answers, the systems can develop a model to find similar English questions. To help Persian CQA systems in providing the answers to the questions, we propose a cross-lingual question retrieval model to retrieve relevant English questions to any input Persian question. In the proposed model, we benefit from a translation model-based retrieval using neural cross-lingual word embedding. Our experiment shows that the proposed model achieves 71.4% MRR and 83.5% success@5 using supervised cross-lingual word embedding.

Question retrieval using combined queries in community question answering

Community question answering (cQA) has emerged as a popular service on the web; users can use it to ask and answer questions and access historical question-answer (QA) pairs. cQA retrieval, as an alternative to general web searches, has several advantages. First, user can register a query in the form of natural language sentences instead of a set of keywords; thus, they can present the required information more clearly and comprehensively. Second, the system returns several possible answers instead of a long list of ranked documents, thereby enhancing the efficient location of the desired answers. Question retrieval from a cQA archive, an essential function of cQA retrieval services, aims to retrieve historical QA pairs relevant to the query question. In this study, combined queries (combined inverted and nextword indexes) are proposed for question retrieval in cQA. The method performance is investigated for two different scenarios: (a) when only questions from QA pairs are used as documents, and (b) when QA pairs are used as documents. In the proposed method, combined indexes are first created for both queries and documents; then, different information retrieval (IR) models are used to retrieve relevant questions from the cQA archive. Evaluation is performed on a public Yahoo! Answers dataset; the results thereby obtained show that using combined queries for all three IR models (vector space model, Okapi model, and language model) improves performance in terms of the retrieval precision and ranking effectiveness. Notably, by using combined indexes when both QA pairs are used as documents, the retrieval and ranking effectiveness of these cQA retrieval models increases significantly.

HCA: Hierarchical Compare Aggregate model for question retrieval in community question answering

We address the problem of finding similar historical questions that are semantically equivalent or relevant to an input query question in community question-answering (CQA) sites. One of the main challenges for this task is that questions are usually too long and often contain peripheral information in addition to the main goals of the question. To address this problem, we propose an end-to-end Hierarchical Compare Aggregate (HCA) model that can handle this problem without using any task-specific features. We first split questions into sentences and compare every sentence pair of the two questions using a proposed Word-Level-Compare-Aggregate model called WLCA-model and then the comparison results are aggregated with a proposed Sentence-Level-Compare-Aggregate model to make the final decision. To handle the insufficient training data problem, we propose a sequential transfer learning approach to pre-train the WLCA-model on a large paraphrase detection dataset. Our experiments on two editions of the Semeval benchmark datasets and the domain-specific AskUbuntu dataset show that our model outperforms the state-of-the-art models.

An integrated retrieval framework for similar questions: Word-semantic embedded label clustering – LDA with question life cycle

Question retrieval is an extremely important research field in Community Question Answering (CQA). Most existing question retrieval methods depend on semantic analysis of questions, whose effectiveness suffers from the short texts of the noise words in the question corpus. In order to recommend the questions with more advanced knowledge to users, the influence of the questions’ popularity should be considered during retrieving questions. To make retrieved questions with both similar semantics and high popularity, we propose an Integrated Retrieval Framework for Similar Questions named Word-semantic Embedded Label Clustering – LDA with Question Life Cycle (WELQLC-QR), consisting of Word-semantic Embedded Label Clustering – LDA (WEL) and Question Life Cycle Optimization Similar Question List Strategy (QLC). Firstly, WEL is proposed for question retrieval from the perspective of semantic matching. It not only overcomes the problem of over-generalization of the semantic information extracted by topic models when facing short questions with multi-levels labels, but also avoids the influence of noise vocabularies during semantic extracting of the questions. Then, based on the internal factors (i.e., the number of comments and answers to the question) and external factors (i.e., programming language ranking information) of questions, QLC constructs a popularity-predicted model to optimize the similar question set searched by WEL, making the final retrieval results both semantically similar and popular. Finally, experiments are conducted on CQADupStack dataset, and results show that the MRR@N of WELQLC-QR model has an average increase of 8.99%, 8.3%, 4.74% and 3.56% compared with that of L-LDA, LC-LDA, BM25 and Word2vec, respectively.