Addressing Community Question Answering in English and Arabic
This paper studies the impact of different types of features applied to learning to re-rank questions in community Question Answering. We tested our models on two datasets released in SemEval-2016 Task 3 on "Community Question Answering". Task 3 targeted real-life Web fora both in English and Arabic. Our models include bag-of-words features (BoW), syntactic tree kernels (TKs), rank features, embeddings, and machine translation evaluation features. To the best of our knowledge, structural kernels have barely been applied to the question reranking task, where they have to model paraphrase relations. In the case of the English question re-ranking task, we compare our learning to rank (L2R) algorithms against a strong baseline given by the Google-generated ranking (GR). The results show that i) the shallow structures used in our TKs are robust enough to noisy data and ii) improving GR is possible, but effective BoW features and TKs along with an accurate model of GR features in the used L2R algorithm are required. In the case of the Arabic question re-ranking task, for the first time we applied tree kernels on syntactic trees of Arabic sentences. Our approaches to both tasks obtained the second best results on SemEval-2016 subtasks B on English and D on Arabic.
Code (0)
등록된 구현이 없습니다.
Tasks
Community Question AnsweringLearning-To-RankMachine TranslationQuestion AnsweringRerankingRe-RankingSimilar Papers 제목 키워드 기반
SemEval-2015 Task 3: Answer Selection in Community Question Answering
Community Question Answering (cQA) provides new interesting research directions to the traditional Question Answering (QA) field, e.g., the exploitation of the interaction between users and the structure of related posts…
Answer SelectionCommunity Question AnsweringQuestion AnsweringQCRI: Answer Selection for Community Question Answering - Experiments for Arabic and English
SemEval-2016 Task 3: Community Question Answering
This paper describes the SemEval--2016 Task 3 on Community Question Answering, which we offered in English and Arabic. For English, we had three subtasks: Question--Comment Similarity (subtask A), Question--Question Simi…
Community Question AnsweringQuestion AnsweringQuestion SimilarityA Survey on non-English Question Answering Dataset
Research in question answering datasets and models has gained a lot of attention in the research community. Many of them release their own question answering datasets as well as the models. There is tremendous progress t…
Cross-Lingual Question AnsweringQuestion AnsweringSurvey