Expert Knowledge-Based Apparel Recommendation Question and Answer System

Aiming at the lack of professional knowledge to guide apparel recommendation,an apparel recommendation method based on image design expert knowledge has been proposed.Then,apparel recommendation knowledge graphs have been created and a apparel recommendation question and answer(Q&A)system has been designed and implemented.The question templates in the apparel recommendation domain were defined,the task of recognizing the named entities of question sentences was completed by the Bi-directional encoder representations from transformer-Bi-directional long short-term memory-conditional random field(BERT-BiLSTM-CRF)model,and the question template with the highest matching degree to the user’s question was obtained by using term frequency-inverse document frequency(TF-IDF)algorithm.The corresponding cypher graph database query statement was generated to retrieve the knowledge graph for answers,and iFLYTEK’s voice application programming interface(API)was called to implement the Q&A.The experimental results have shown that the Q&A system has a high accuracy rate and application value in the field of apparel recommendations.

Question classification in question answering based on real-world web data sets

To improve question answering （QA） performance based on real-world web data sets,a new set of question classes and a general answer re-ranking model are defined.With pre-defined dictionary and grammatical analysis,the question classifier draws both semantic and grammatical information into information retrieval and machine learning methods in the form of various training features,including the question word,the main verb of the question,the dependency structure,the position of the main auxiliary verb,the main noun of the question,the top hypernym of the main noun,etc.Then the QA query results are re-ranked by question class information.Experiments show that the questions in real-world web data sets can be accurately classified by the classifier,and the QA results after re-ranking can be obviously improved.It is proved that with both semantic and grammatical information,applications such as QA, built upon real-world web data sets, can be improved,thus showing better performance.

Triple Multimodal Cyclic Fusion and Self-Adaptive Balancing for Video Q&A Systems

Performance of Video Question and Answer(VQA)systems relies on capturing key information of both visual images and natural language in the context to generate relevant questions’answers.However,traditional linear combinations of multimodal features focus only on shallow feature interactions,fall far short of the need of deep feature fusion.Attention mechanisms were used to perform deep fusion,but most of them can only process weight assignment of single-modal information,leading to attention imbalance for different modalities.To address above problems,we propose a novel VQA model based on Triple Multimodal feature Cyclic Fusion(TMCF)and Self-AdaptiveMultimodal Balancing Mechanism(SAMB).Our model is designed to enhance complex feature interactions among multimodal features with cross-modal information balancing.In addition,TMCF and SAMB can be used as an extensible plug-in for exploring new feature combinations in the visual image domain.Extensive experiments were conducted on MSVDQA and MSRVTT-QA datasets.The results confirm the advantages of our approach in handling multimodal tasks.Besides,we also provide analyses for ablation studies to verify the effectiveness of each proposed component.

A FRAMEWORK FOR DOMAIN-SPECIFIC NATURAL LANGUAGE INFORMATION BROKERAGE

Service providers - from public institutions to primary care facilities - need to constantly attend toclients＇ inquiries to provide useful information and directive guidelines. Ensuring high quality serviceis challenging as it not only demands detailed domain-specific knowledge, but also the ability toquickly understand the clients＇ issues through their diverse - and often casual - descriptions. Thispaper aims to provide a framework for the development of an automated information broker agent whoperforms the task of a helper. The main task of the agent is to interact with the client and direct them toobtain further services that cater their personalized need. To do so, the agent should accomplish asequence of tasks that include natural language inquiry, knowledge gathering, reasoning, and givingfeedback; in this way, it simulates a human helper to engage in interaction with the client. Theframework combines a question-answering reasoning mechanism while utilizing domain-specificknowledge base. When the users cannot describe clearly their needs, the system tries to narrow downthe possibilities by an iterative question-answering process, until it eventually identifies the target. Inrealizing our framework, we make a proof-of-concept project, M andy, a primary care chatbot systemcreated to assist healthcare staffs by automating the patient intake process. We describe in detail thesystem functionalities and design of the system, and evaluate our proof-of-concept on benchmark casestudies.