Childrens Disclosure of Sex Abuse: A New Approach to Answering Elusive QuestionsDickinson, Jason JDel Russo, JosephD'Urso, Anthony
Figure 1. Illustration of the multi-role capabilities in the KG-EGV framework. KG-EGV incorporates multiple roles within large language models (LLMs) to perform generation, re-ranking, evaluation, and verification. Sentence Segmentation and Graph Retrieval: Segmenting questions and retrieving relevant...
Visual Question Answering (VQA) models fail catastrophically on questions related to the reading of text-carrying images. However, TextVQA aims to answer questions by understanding the scene texts in an image–question context, such as the brand name of a product or the time on a clock from ...
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In the query complexity identification stage, we developed a query complexity classifier to determine whether the user query can be decomposed into multiple questions. In the adaptive retrieval stage, either a single-step strategy or a multi-step strategy is applied based on the complexity of the ...
We envision two scenarios when the knowledge graph is useful for architectural design in AC: realizing so-called KGQA, taking the knowledge graph as input to answer AC-related questions such as the mechanical properties of printed building materials; and SWT-based information retrieval for dedicated...
Complex Question Answering over Knowledge Graph (C-KGQA) seeks to solve complex questions using knowledge graphs. Currently, KGQA systems achieve great success in answering simple questions, while complex questions still present challenging issues. As a result, an increasing number of novel methods have...
On the one hand, it can answer questions in a specific field. On the other hand, it can also answer common sense knowledge related to knowledge in the specific field. Therefore, in the question answering process, we try to combine domain-specific knowledge graphs and open-domain knowledge ...
Figure 3. Comparative example of POS tagging in bridge management questions. The former is the POS tagging result of Jieba tool, and the latter is the POS tagging result after adding the domain dictionary. Table 1. The examples of entity and POS labels. There are six types of entity and ...
However, the former did not release any data while the latter only released 454 questions for public use, while we publicize a large-scale dataset to promote more powerful deep models. Table 2. Comparison of our dataset with existing medical QA datasets. In terms of the answer format, “...