A Tag2Vec Approach for Questions Tag Suggestion on Community Question Answering Sites

Roy, Pradeep Kumar; Singh, Jyoti

doi:10.1007/978-3-319-96133-0_13

Cited by 7 publications

(4 citation statements)

References 17 publications

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“…Rouge: As an evaluation criterion for text summarization tasks, Rouge is the most prevalent. Equation (10) formulates the recall version of Rouge-N. Taking 𝑆 as the original sequence and 𝑆 ̂ as the generated one, the numerator enumerates the overlapping 𝑁-grams between them, and the denominator counts the total of 𝑁-grams in 𝑆.…”

Section: Evaluation Metricsmentioning

confidence: 99%

“…𝑔𝑟𝑎𝑚 𝑁 ∈𝑆 (10) Rouge-N values fall between 0 and 1, with closer values to 1 indicating more lexical overlap and a better quality of the generated sequences. Although Rouge-N ensures the model captures the reference sequence's information as much as possible, it does not consider the word order when 𝑁 is small.…”

Section: Evaluation Metricsmentioning

confidence: 99%

“…Nevertheless, many questions may not be answered immediately or even remain unanswered forever. This issue may arise from the proliferation of newly arrived questions [6], too long or ambiguous questions that fail to appeal to relevant experts [7]- [9], improper categorization of questions or users [10]- [12], and the like. Hence, researchers have strived to enhance CQA platforms from various aspects and make them more efficient.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

ReQuEST: A Small-Scale Multi-Task Model for Community Question-Answering Systems

Aftabi,

Seyyedi,

Maleki

et al. 2024

IEEE Access

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The burgeoning popularity of community question-answering platforms as an informationseeking strategy has prompted researchers to look for ways to save response time and effort, among which question entailment recognizing, question summarizing, and question tagging are prominent. However, none has investigated the implicit relations between these tasks and the benefits their interaction could provide. In this study, ReQuEST, a novel multi-task model based on bidirectional auto-regressive transformers (BART), is introduced to recognize question entailment, summarize questions respecting given queries, and tag questions with primary topics, simultaneously. ReQuEST comprises one shared encoder representing input sequences, two half-shared decoders providing intermediate presentations, and three task-specific heads producing summaries, tags, and entailed questions. A lightweight fine-tuning technique and a weighted loss function help us learn model parameters efficiently. With roughly 187k learning parameters, ReQuEST is almost half the size of BARTlarge and is two-thirds smaller than its multi-task counterparts. Empirical experiments on standard summarization datasets reveal that ReQuEST outperforms competitors on Debatepedia with a Rouge-L of 46.77 and has persuasive performance with a Rouge-L of 37.37 on MeQSum. On MediQA-RQE as a medical benchmark for entailment recognition, ReQuEST is also comparable in accuracy with state-of-the-art systems without being pre-trained on domain-specific datasets.

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Section: Evaluation Metricsmentioning

confidence: 99%

Section: Evaluation Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

ReQuEST: A Small-Scale Multi-Task Model for Community Question-Answering Systems

Aftabi,

Seyyedi,

Maleki

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Identifying such users and developing ways to stop them also need immediate attention, not as reported for increasing number of participants adopting counting‐based secret sharing [191]. Besides topic modelling, the tag suggestion for a new question has attracted very limited research [192]. Hence, in the future, interested scholars may consider these topics to improve the content quality of CQAs.…”

Section: Open Research Issues Directions and Challengesmentioning

confidence: 99%

Analysis of community question‐answering issues via machine learning and deep learning: State‐of‐the‐art review

Roy

Singh

Banerjee

et al. 2022

CAAI Trans on Intel Tech

Self Cite

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Over the last couple of decades, community question-answering sites (CQAs) have been a topic of much academic interest. Scholars have often leveraged traditional machine learning (ML) and deep learning (DL) to explore the ever-growing volume of content that CQAs engender. To clarify the current state of the CQA literature that has used ML and DL, this paper reports a systematic literature review. The goal is to summarise and synthesise the major themes of CQA research related to (i) questions, (ii) answers and (iii) users. The final review included 133 articles. Dominant research themes include question quality, answer quality, and expert identification. In terms of dataset, some of the most widely studied platforms include Yahoo! Answers, Stack Exchange and Stack Overflow. The scope of most articles was confined to just one platform with few cross-platform investigations. Articles with ML outnumber those with DL. Nonetheless, the use of DL in CQA research is on an upward trajectory. A number of research directions are proposed.

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Predicting answer acceptability for question-answering system

Roy

2023

Int J Digit Libr

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A Tag2Vec Approach for Questions Tag Suggestion on Community Question Answering Sites

Cited by 7 publications

References 17 publications

ReQuEST: A Small-Scale Multi-Task Model for Community Question-Answering Systems

ReQuEST: A Small-Scale Multi-Task Model for Community Question-Answering Systems

Analysis of community question‐answering issues via machine learning and deep learning: State‐of‐the‐art review

Predicting answer acceptability for question-answering system

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