threads of comments, where only a small portion of them are actually valid answers. Although the idea of receiving a direct, targeted response from other users is very attractive, it is not rare to see long. In fact, forums allow users to freely ask questions and expect answers from the community. In recent years, forums offering community Question Answering (cQA) services gained popularity on the web, as they offer a new opportunity for users to search and share knowledge. These scores are better than the baselines, especially for subtasks A-C. The best systems achieved an official score (MAP) of 88.43, 47.22, 15.46, and 61.16 in subtasks A, B, C, and D, respectively. A variety of approaches and features were used by the participating systems to address the different subtasks. Unfortunately, no teams participated in subtask E. A total of 23 teams participated in the task, and submitted a total of 85 runs (36 primary and 49 contrastive) for subtasks A-D. a new subtask E in order to enable experimentation with Multi-domain Question Duplicate Detection in a larger-scale scenario, using StackExchange subforums. This year, we reran the four subtasks from SemEval-2016:(A) Question-Comment Similarity,(B) Question-Question Similarity,(C) Question-External Comment Similarity, and (D) Rerank the correct answers for a new question in Arabic, providing all the data from 20 for training, and fresh data for testing. We describe SemEval-2017 Task 3 on Community Question Answering. The model shows significant improvement over the state-of-the-artwork on sentence similarity tasks. Experiments were carried out on Quora (QQP) and Stack Exchange cQA dataset with training sets of different sizes and word vectors of different dimensionalities. Then sBERT is used to assess the similarity between the questions. (ii) Fine-tuning: we fine-tuned BERT model on STS and SNLI data, which employs Siamese network architectures to generate semantically meaningful sentence embeddings. Then Siamese LSTM (sLSTM) is used to find the semantic similarity among the questions. We followed two approaches (i) Feature-based: the question embedding is created using four forms of word embeddings and an ensemble of all four. To address this issue, this work proposes the model for discovering the semantic similarity among the cQA questions. Because of this redundancy, the responses are scattered through various variations of the same question that results in unsatisfactory search results to a specific question. The probability of redundancy in questions has significantly increased due to the increasing influx of users on different cQA forums such as Quora, Stack overflow, etc. The experimental results on three widely used datasets demonstrate that our proposed method is effective and outperforms the existing baselines significantly. Moreover, the model uses variational autoencoders (VAE) in a multi-task learning process with a classifier to produce class-specific representations for answers. It also uses the question category for producing context-aware representations for questions and answers. We also learn a latent-variable model for learning the representations of the question and answer, jointly optimizing generative and discriminative objectives. In this paper, we propose a novel answer selection method in CQA by using the knowledge embedded in KGs. Despite the obvious usefulness of commonsense and factual information in the KGs, to the best of our knowledge, KGs have been rarely integrated into the task of answer selection in community question answering (CQA). With the increasing popularity of knowledge graph (KG), many applications such as sentiment analysis, trend prediction, and question answering use KG for better performance. These scores are better than the base-lines, especially for subtasks A–C. A total of 23 teams participated in the task, and submitted a total of 85 runs (36 primary and 49 contrastive) for sub-tasks A–D. Additionally, we added a new subtask E in order to enable experimentation with Multi-domain Question Duplicate Detection in a larger-scale scenario, using StackExchange subforums. This year, we reran the four subtasks from SemEval-2016: (A) Question–Comment Similarity, (B) Question–Question Similarity, (C) Question– External Comment Similarity, and (D) Rerank the correct answers for a new question in Arabic, providing all the data from 20 for training, and fresh data for testing. We describe SemEval2017 Task 3 on Community Question Answering.
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