BACKGROUND

Search engines are reasonably proficient at responding to many different types of queries. For instance, existing search algorithms provide good precision and recall for most queries, i.e., the documents returned by a search generally are relevant to the query and relatively few relevant documents are missed. However, in some cases, the answer to a query is not necessarily found in a single document returned for a search query. Efforts to accurately answer queries that involve reasoning over multiple different documents have met with limited success.

SUMMARY

This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.

The description generally relates to techniques for natural language processing. One example includes a method or technique that can be performed on a computing device. The method or technique can include receiving a query and retrieving a plurality of result documents related to the query. The method or technique can also include establishing an evidence graph by linking individual result documents. The method or technique can also include obtaining contextualized semantic representations for individual words in a second result document by propagating inter-document attention from a first result document that is linked to the second result document in the evidence graph. The method or technique can also include processing the contextualized semantic representations of the individual words in the second result document using a machine learning model to obtain an answer to the query. The method or technique can also include outputting the answer in response to the query.

Another example includes a computer-readable storage medium storing computer-readable instructions which, when executed by a hardware processing unit, cause the hardware processing unit to perform acts. The acts can include obtaining a first document and a second document. The acts can also include propagating attention from the first document to the second document. The acts can also include producing contextualized semantic representations of individual words in the second document based at least on the propagating. The contextualized semantic representations can provide a basis for performing one or more natural language processing operations.

Another example includes a system having a hardware processing unit and a storage resource storing computer-readable instructions. When executed by the hardware processing unit, the computer-readable instructions can cause the hardware processing unit to receive a query. The computer-readable instructions can also cause the hardware processing unit to obtain a plurality of search results that are responsive to the query and process the plurality of search results using a transformer having an inter-document attention mechanism to obtain an answer to the query. The computer-readable instructions can also cause the hardware processing unit to respond to the query with the answer.

The above listed examples are intended to provide a quick reference to aid the reader and are not intended to define the scope of the concepts described herein.

