MARGEN: Marathi Question Answering Generative Conversation Model
نویسندگان
چکیده
منابع مشابه
Neural Generative Question Answering
This paper presents an end-to-end neural network model, named Neural Generative Question Answering (GENQA), that can generate answers to simple factoid questions, both in natural language. More specifically, the model is built on the encoder-decoder framework for sequence-to-sequence learning, while equipped with the ability to access an embedded knowledge-base through an attention-like mechani...
متن کاملQuestion Answering System Using Ontology in Marathi Language
Humans are always in a quest to extract information related to some topic or entity. Question answering system helps user to find the precise answer of the question articulated in natural language. Question answering system provides explicit, concise and accurate answer to user questions rather than providing set of relevant documents or web pages as answers as most of the information retrieval...
متن کاملInvestigating Embedded Question Reuse in Question Answering
The investigation presented in this paper is a novel method in question answering (QA) that enables a QA system to gain performance through reuse of information in the answer to one question to answer another related question. Our analysis shows that a pair of question in a general open domain QA can have embedding relation through their mentions of noun phrase expressions. We present methods f...
متن کاملA Unified Query-based Generative Model for Question Generation and Question Answering
We propose a query-based generative model for solving both tasks of question generation (QG) and question answering (QA). The model follows the classic encoderdecoder framework. The encoder takes a passage and a query as input then performs query understanding by matching the query with the passage from multiple perspectives. The decoder is an attention-based Long Short Term Memory (LSTM) model...
متن کاملNeural Contextual Conversation Learning with Labeled Question-Answering Pairs
Neural conversational models tend to produce generic or safe responses in different contexts, e.g., reply “Of course” to narrative statements or “I don’t know” to questions. In this paper, we propose an end-to-end approach to avoid such problem in neural generative models. Additional memory mechanisms have been introduced to standard sequence-to-sequence (seq2seq) models, so that context can be...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Advances in computer science research
سال: 2023
ISSN: ['2352-538X']
DOI: https://doi.org/10.2991/978-94-6463-136-4_46