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In this paper, we present a neural network based framework for answering non-factoid questions. The framework consists of two main components: Answer Retriever and Answer Ranker. In the first component, we leverage off-the-shelf retrieval models (e.g. bm25) to retrieve a pool of candidate answers regarding to the input question. Answer Ranker is then used to select the most suitable answer. In this work, we adopt two typical deep learning based frameworks for our Answer Ranker component. One is based on Siamese architecture and the other is the Compare-Aggregate framework. The Answer Ranker component is evaluated separately based on popular answer selection datasets. Our overall system is evaluated using FiQA dataset, a newly released dataset for financial domain and shows promising results.

In this paper, we describe the development of an end-to-end factoid question answering system for the Vietnamese language. This system combines both statistical models and ontology-based methods in a chain of processing modules to provide high-quality mappings from natural language text to entities. We present the challenges in the development of such an intelligent user interface for an isolating language like Vietnamese and show that techniques developed for inflectional languages cannot be applied "as is". Our question answering system can answer a wide range of general knowledge questions with promising accuracy on a test set., In the proceedings of the HQA'18 workshop, The Web Conference Companion, Lyon, France

In this demo, we present VIZ-Wiki, a browser extension which generates an overview of summarizable threads in Question Answering forums. It reduces a user's effort to go through lengthy text-based, sarcastic and highly critiqued answers. Our tool can be used to collect community opinion from popular discussion sites like Quora, Yahoo! Answers, Reddit etc. as well as topic-centric ones such as Askubuntu, Stackoverflow. We rely on textual information of these forums to extract insightful summaries for a reader. VIZ-Wiki provides users a pie-graph view marking popular choices when such a question link is raised. A button guides them to detailed statistics and relevant list of answers. It further highlights sentences relevant to an answer choice in the text. VIZ-Wiki deals with answers contradicted by other users, prioritizes highly-recommended ones and avoids sarcasm. We test our model on the factoid questions dataset of Yahoo! Answers and obtain a macro precision of 0.6 on displayed answers and a macro recall of 0.69, beating the baseline significantly. To the best of our knowledge, VIZ-Wiki is the first attempt to visualize answers for questions in community question answering services. In the spirit of reproducibility, we have released the code and a demonstration video public at \urlhttp://goo.gl/cyx3EF and \urlhttps://youtu.be/XNmRa_jtmC8 respectively

This paper presents an outline of an Ontological and Se- mantic understanding-based model (SEMONTOQA) for an open-domain factoid Question Answering (QA) system. The outlined model analyses unstructured English natural lan- guage texts to a vast extent and represents the inherent con- tents in an ontological manner. The model locates and ex- tracts useful information from the text for various question types and builds a semantically rich knowledge-base that is capable of answering different categories of factoid ques- tions. The system model converts the unstructured texts into a minimalistic, labelled, directed graph that we call a Syntactic Sentence Graph (SSG). An Automatic Text In- terpreter using a set of pre-learnt Text Interpretation Sub- graphs and patterns tries to understand the contents of the SSG in a semantic way. The system proposes a new fea- ture and action based Cognitive Entity-Relationship Net- work designed to extend the text understanding process to an in-depth level. Application of supervised learning allows the system to gradually grow its capability to understand the text in a more fruitful manner. The system incorpo- rates an effective Text Inference Engine which takes the re- sponsibility of inferring the text contents and isolating enti- ties, their features, actions, objects, associated contexts and other properties, required for answering questions. A similar understanding-based question processing module interprets the user’s need in a semantic way. An Ontological Mapping Module, with the help of a set of pre-defined strategies de- signed for different classes of questions, is able to perform a mapping between a question’s ontology with the set of ontologies stored in the background knowledge-base. Em- pirical verification is performed to show the usability of the proposed model. The results achieved show that, this model can be used effectively as a semantic understanding based alternative QA system.

Recent initiatives in IR community have shown the importance of going beyond factoid Question Answering (QA) in order to design useful real-world applications. Questions asking for descriptions or explanations are much more difficult to be solved, e.g., the machine learning models cannot focus on specific answer words or their lexical type. Thus, researchers have started to explore powerful methods for feature engineering. Two of the most promising methods are convolution tree kernels (CTKs) and convolutional neural networks (CNNs) as they have been shown to obtain high performance in the task of answer sentence selection in factoid QA. In this paper, we design state-of-the-art models for non-factoid QA also carried out on noisy data. In particular, we study and compare models for comment selection in a community QA (cQA) scenario, where the majority of questions regard descriptions or explanations. To deal with such complex task, we incorporate relational information holding between questions and comments as well as domain-specific features into both convolutional models above. Our experiments on a cQA corpus show that both CTK and CNN achieve the state of the art, also according to a direct comparison with the results obtained by the best systems of the SemEval cQA challenge.