We address the problem of answering new questions in community forums , by selecting suitable answers to already asked questions . We approach the task as an answer ranking problem , adopting a pairwise neural network architecture that selects which of two competing answers is better . We focus on the utility of the three types of similarities occurring in the triangle formed by the original question , the related question , and an answer to the related comment , which we call relevance , relatedness , and appropriateness . Our proposed neural network models the interactions among all input components using syntactic and semantic embeddings , lexical matching , and domain-specific features . It achieves state-of-the-art results , showing that the three similarities are important and need to be modeled together . Our experiments demonstrate that all feature types are relevant , but the most important ones are the lexical similarity features , the domain-specific features , and the syntactic and semantic embeddings . In recent years , community Question Answering ( cQA ) forums , such as StackOverflow , Quora , Qatar Living , etc . , have gained a lot of popularity as a source of knowledge and information . These forums typically organize their content in the form of multiple topic-oriented question-comment threads , where a question posed by a user is followed by a list of other users ' comments , which intend to answer the question . Many of such on-line forums are not moderated , which often results in ( a ) noisy and ( b ) redundant content , as users tend to deviate from the question and start asking new questions or engage in conversations , fights , etc . Web forums try to solve problem ( a ) in various ways , most often by allowing users to up / downvote answers according to their perceived usefulness , which makes it easier to retrieve useful answers in the future . Unfortunately , this negatively penalizes recent comments , which might be the most relevant and updated ones . This is due to the time it takes for a comment to accumulate votes . Moreover , voting is prone to abuse by forum trolls ( Mihaylov et al . , 2015 ; Mihaylov and Nakov , 2016a ) . Problem ( b ) is harder to solve , as it requires that users verify that their question has not been asked before , possibly in a slightly different way . This search can be hard , especially for less experienced users as most sites only offer basic search , e.g. , a site search by Google . Yet , solving problem ( b ) automatically is important both for site owners , as they want to prevent question duplication as much as possible , and for users , as finding an answer to their questions without posting means immediate satisfaction of their information needs . In this paper , we address the general problem of finding good answers to a given new question ( referred to as original question ) in one such community-created forum . More specifically , we use a pairwise deep neural network to rank comments retrieved from different question-comment threads according to their relevance as answers to the original question being asked . A key feature of our approach is that we investigate the contribution of the edges in the triangle formed by the pairwise interactions between the original question , the related question , and the related comments to rank comments in a unified fashion . Additionally , we use three different sets of features that capture such similarity : lexical , distributed ( semantics / syntax ) , and domain-specific knowledge . The experimental results show that addressing the answer ranking task directly , i.e. , modelling only the similarity between the original question and the answer-candidate comments , yields very low results . The other two edges of the triangle are needed to obtain good results , i.e. , the similarity between the original question and the related question and the similarity between the related question and the related comments . Both aspects add significant and cumulative improvements to the overall performance . Finally , we show that the full network , including the three pairs of similarities , outperforms the state-of-the-art on a benchmark dataset . The rest of the paper is organized as follows : Section 2 discusses the similarity triangle in answer ranking for cQA , Section 3 presents our pairwise neural network model for answering new questions in community forums , which integrates multiple levels of interaction , Section 4 describes the features we used , Section 5 presents our evaluation setup , the experiments and the results , Section 6 discusses some related work , and Section 7 wraps up the paper with a brief summary of the contributions and some possible directions for future work . 2 The Similarity Triangle in cQA Figure 1 presents an example illustrating the similarity triangle that we use when solving the answer ranking problem in cQA . In the figure , q stands for the new question , q is an existing related question , and c is a comment within the thread of question q . The edge qc relates to the main cQA task addressed in this paper , i.e. , deciding whether a comment for a potentially related question is a good answer to the original question . We will say that the relation captures the relevance of c for q. The edge qq represents the similarity between the original and the related questions . We will call this relation relatedness . We presented a neural-based approach to a novel problem in cQA , where given a new question , the task is to rank comments from related questionthreads according to their relevance as answers to the original question . We explored the utility of three types of similarities between the original question , the related question , and the related comment . We adopted a pairwise feed-forward neural network architecture , which takes as input the original question and two comments together with their corresponding related questions . This allowed us to study the impact and the interaction effects of the question-question relatedness and commentto-related question appropriateness relations when solving the primary cQA relevance task . The large performance gains obtained from using relatedness features show that question-question similarity plays a crucial role in finding relevant comments ( +30 MAP points ) . Yet , including appropriateness relations is needed to achieve state-of-the-art results ( +3.3 MAP ) on benchmark datasets . We also studied the impact of several types of features , especially domain-specific features , but also lexical features and syntactic embeddings . We observed that lexical similarity MTE features prove the most important , followed by domain-specific features , and syntactic and semantic embeddings . Overall , they all showed to be necessary to achieve state-of-the-art results . In future work , we plan to use the labels for subtasks A and B , which are provided in the datasets in order to pre-train the corresponding components of the full network for answer ranking . We further want to apply a similar network to other semantic similarity problems , such as textual entailment .

Challenge: Detecting duplicated questions in forums is difficult while it offers merits for owners to prevent them and users from finding answers to questions without asking.
Approach: They propose to feed related questions and the corresponding answers coupled with the original question to a pairwise model to approach an answer ranking problem.
Outcome: The proposed model achieves the state-of-the-art and they find that lexical similarity contributes more to the final performance than other similarity measures.