Community Question Answering
42 papers with code • 2 benchmarks • 5 datasets
Community question answering is the task of answering questions on a Q&A forum or board, such as Stack Overflow or Quora.
Benchmarks
These leaderboards are used to track progress in Community Question Answering
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
Quora Question Pairs
AmazonQA
ANTIQUE
Latest papers
Answer Retrieval in Legal Community Question Answering
Furthermore, we propose LegalQA: a real-world benchmark dataset for evaluating answer retrieval in the legal domain.
Reference Free Domain Adaptation for Translation of Noisy Questions with Question Specific Rewards
babangain/unsup_questions_translation
•
•
Translating questions using Neural Machine Translation (NMT) poses more challenges, especially in noisy environments, where the grammatical correctness of the questions is not monitored.
SE-PQA: Personalized Community Question Answering
pkasela/se-pqa
•
•
Personalization in Information Retrieval is a topic studied for a long time.
CHQ-Summ: A Dataset for Consumer Healthcare Question Summarization
The quest for seeking health information has swamped the web with consumers' health-related questions.
Community Question Answering Entity Linking via Leveraging Auxiliary Data
Community Question Answering (CQA) platforms contain plenty of CQA texts (i. e., questions and answers corresponding to the question) where named entities appear ubiquitously.
Divide and Conquer: Text Semantic Matching with Disentangled Keywords and Intents
rowitzou/dc-match
•
•
Findings (ACL) 2022
Text semantic matching is a fundamental task that has been widely used in various scenarios, such as community question answering, information retrieval, and recommendation.
Expert Finding in Legal Community Question Answering
In the legal domain, there is a large knowledge gap between the experts and the searchers, and the content on the legal QA websites consist of a combination formal and informal communication.
HeteroQA: Learning towards Question-and-Answering through Multiple Information Sources via Heterogeneous Graph Modeling
Most of the CQA methods only incorporate articles or Wikipedia to extract knowledge and answer the user's question.
AnswerSumm: A Manually-Curated Dataset and Pipeline for Answer Summarization
alex-fabbri/answersumm
•
•
NAACL 2022
One goal of answer summarization is to produce a summary that reflects the range of answer perspectives.
Question Answering over Electronic Devices: A New Benchmark Dataset and a Multi-Task Learning based QA Framework
abhi1nandy2/emnlp-2021-findings
•
•
Findings (EMNLP) 2021
Answering questions asked from instructional corpora such as E-manuals, recipe books, etc., has been far less studied than open-domain factoid context-based question answering.
