Using sequence to sequence algorithms for query expansion has not been explored yet in Information Retrieval literature nor in Question-Answering's. We tried to fill this gap in the literature with a custom Query Expansion engine trained and tested on open datasets. Starting from open datasets, we built a Query Expansion training set using sentence-embeddings-based Keyword Extraction. We therefore assessed the ability of the Sequence to Sequence neural networks to capture expanding relations in the words embeddings' space.
Paraphrase generation is a very close field. Generating new utterances carrying the same meaning expands the initial query and highly increases the robustness of a search-based chatbot @cite_5 @cite_3 .
@cite_5 @cite_3
2395170394 1980519283
This paper presents an approach to creating intelligent conversational agents that are capable of returning appropriate responses to natural language input. Our approach consists of using a supervised learning algorithm in combination with different NLP algorithms in training the system to identify paraphrases of the user’s question stored in a database. When tested on a data set consisting of questions and answers for a current conversational agent project, our approach returned an accuracy score of 79.15 , a precision score of 77.58 and a recall score of 78.01 . Paraphrases, which stem from the variety of lexical and grammatical means of expressing meaning available in a language, pose challenges for a sentence generation system. In this paper, we discuss the generation of paraphrases from predicate argument structure using a simple, uniform generation methodology. Central to our approach are lexico-grammatical resources which pair elementary semantic structures with their syntactic realization and a simple but powerful mechanism for combining resources.