BACKGROUND

Textual entailment relates to a directional relationship between text fragments in a text document, based on a natural language processing operation. The directional relationship in textual entailment may be based on mimicking the cognitive comprehension of a human being. For example, the directional relationship may hold whenever the truth of one text fragment follows from another text fragment. Such a relationship may be used for generating contextual answer responses to a user question and passage input.

Many approaches have been considered for generating contextual answer responses. These approaches include, for example, Applications like Machine Reading Comprehension (MRC) use a SQuAD 2.0 dataset to train a Bi-Directional Attention Flow (BiDAF) model for the task of generating contextual answer responses to a user question and passage input. These models may deploy a combination of query generation, a keyword search using a knowledge base and answer ranking to determine answer information. Various other models used in reading comprehension may have been trained to provide abbreviated answer responses for a given question-passage pair.

However, these models may lack the availability of qualitative sentence responses in the answer string. For example, various questions may require an answer including information regarding various people, places, ideas or things. Such questions may not be adequately processed by many approaches presently deployed for generating contextual answer responses.

Therefore, to ensure efficiency and completeness, an answer entailment technique may be required to ensure that a natural language text response including natural language semantics be provided for a user question based on an input passage. There is a requirement for the creation of an approach for generating contextual answer responses based on a dataset such as, for example, the SQuAD dataset, to include phrasal responses. There is also a need for an answer phrasing system, which may transform the entailment operations for generating insight-driven semantic triplets and phrase an answer sentence therefrom, such that the answers generated may comprise a noun to verb grammatical connection for emulating a natural language response.

Accordingly, a technical problem with the currently available systems for generation of contextual answer responses to a user question and passage input is that they may be inefficient, inaccurate, and/or not scalable. There is a need for a sentence phrasing system that may account for the various factors mentioned above, amongst others, for establishing multi-dimensional grammatical relationships emulating natural language responses between questions posed by a user and various text fragments from a text document in an efficient, and cost-effective manner.

