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

Zahedi, Mohammad Sadegh; Rahgozar, Maseud; Zoroofi, Reza Aghaeizadeh

doi:10.1016/j.ipm.2020.102318

Cited by 24 publications

(7 citation statements)

References 43 publications

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“…Similar questions are identified in [28] with the help of a siamese network which consists of two parts with the same architecture and weight along with a convolution layer. A hierarchical compare-aggregate model for question retrieval in CQA is introduced by [9]. Questions are split into sentences and every sentence pair of the two questions is compared using a proposed word-level compare-aggregate model.…”

Section: Related Workmentioning

confidence: 99%

“…Through the examination of metadata usage in the topic model-based approaches, [5] relied on question titles and categories, while [7,23] utilized both titles and answers. The investigation into metadata usage within the third group reveals that [8,9,30] focused on utilizing subjects, [10,26,27] relied on answers, [24] incorporated titles and categories, [31] utilized both subjects and answers, and [28,29] exclusively utilized question text. It's worth noting that the term "question subject" is interchangeably referred to as "question title" in some literature and is employed either independently or concatenated with the question text.…”

Section: Related Workmentioning

confidence: 99%

“…In previous works, evaluation metrics such as Mean Average Precision (MAP), Mean Reciprocal Rank (MRR), accuracy, Precision at k (P@k), F1 score, and some task-oriented metrics [48] have been commonly employed to assess the effectiveness of retrieval systems. For the evaluation, we use the MAP measure because it is the evaluation measure at SemEval-2017-task3 and is widely used in the literature for the evaluation of question retrieval performance in CQA [9,29,31,32].…”

Section: A Datasetmentioning

confidence: 99%

“…In these methods, a topic model is trained and the similarity between words is calculated in the topic space to find the similarity between words. The third category contains the methods based on neural networks and deep learning [8][9][10]. This article is classified both in the first category and the second category.…”

Section: Introductionmentioning

confidence: 99%

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

Ghasemi,

Shakery

2024

IEEE Access

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Community Question Answering (CQA) forums such as Yahoo! Answers and Stack Overflow have become popular. The main goal of a CQA is to provide the most suitable answer in the shortest possible time. Since there is a reach archive of answered questions, similar question retrieval has received much attention intending to answer questions immediately after asking. One of the main challenges in this task is the lexical gap between questions, which refers to the discrepancies between the terminologies used by users asking questions. In this paper, we use metadata and two transformer-based techniques to improve the translation-based language model as a traditional technique addressing the lexical gap in retrieval systems. To overcome the lexical gap problem, additional context and information about the questions can help. Metadata is a rich source of information that refers to supplementary data associated with each question. Subject, category, and answer are metadata used in this article. To leverage these metadata, two transformerbased methods are employed. First, to utilize category information, we build category-specific dictionaries to obtain more accurate translation probabilities. A BERT model predicts the categories of the questions. Second, to utilize answer information, we propose a question expansion technique. Expansion is done by a transformer-based model using a retrieval-augmented generation (RAG) model to generate answers and expand new questions with corresponding answers. Finally, candidate questions are ranked according to their similarity to the expanded new question. Our proposed method achieves 51.47 in terms of MAP, outperforming all state-of-the-art approaches in question retrieval.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: A Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Ghasemi,

Shakery

2024

IEEE Access

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“…This type of Question Answering System has access to more data to extract the answer. The closed-domain question answering systems are domain-specific [2,9,45]. Closeddomain question answering systems answers from either a pre-structured database or the collection of domain-specific natural language documents.…”

Section: Introductionmentioning

confidence: 99%

Automatic question-answer pairs generation and question similarity mechanism in question answering system

2021

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With the swift growth of the information over the past few years, taking full benefit is increasingly essential. Question Answering System is one of the promising methods to access this much information. The Question Answering System lacks humans’ common sense and reasoning power and cannot identify unanswerable questions and irrelevant questions. These questions are answered by making unreliable and incorrect guesses. In this paper, we address this limitation by proposing a Question Similarity mechanism. Before a question is posed to a Question-Answering system, it is compared with possible generated questions of the given paragraph, and then a Question Similarity Score is generated. The Question Similarity mechanism effectively identifies the unanswerable and irrelevant questions. The proposed Question Similarity mechanism incorporates a human way of reasoning to identify unanswerable and irrelevant questions. This mechanism can avoid the unanswerable and irrelevant questions altogether from being posed to the Question Answering system. It helps the Question Answering Systems to focus only on the answerable questions to improve their performance. Along with this, we introduce an application of the Question Answering System that generates the question-answer pairs given a passage and is useful in several fields.

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Multi-view pre-trained transformer via hierarchical capsule network for answer sentence selection

Li,

Yang,

Sun

et al. 2024

Appl Intell

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

Cited by 24 publications

References 43 publications

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

Automatic question-answer pairs generation and question similarity mechanism in question answering system

Multi-view pre-trained transformer via hierarchical capsule network for answer sentence selection

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