Abstract: In implementations of systems for generating indications of relationships between electronic documents, a processing device implements a relationship system to segment text of electronic documents included in a document corpus into segments. The relationship system determines a subset of the electronic documents that includes electronic document pairs having a number of similar segments that is greater than a threshold number. The similar segments are identified using locality sensitive hashing. The electronic document pairs are classified as related documents or unrelated documents using a machine learning model that receives a pair of electronic documents as an input and generates an indication of a classification for the pair of electronic documents as an output. Indications of relationships between particular electronic documents included in the subset are generated based at least partially on the electronic document pairs that are classified as related documents.

Abstract: In implementations of systems for generating indications of relationships between electronic documents, a processing device implements a relationship system to segment text of electronic documents included in a document corpus into segments. The relationship system determines a subset of the electronic documents that includes electronic document pairs having a number of similar segments that is greater than a threshold number. The similar segments are identified using locality sensitive hashing. The electronic document pairs are classified as related documents or unrelated documents using a machine learning model that receives a pair of electronic documents as an input and generates an indication of a classification for the pair of electronic documents as an output. Indications of relationships between particular electronic documents included in the subset are generated based at least partially on the electronic document pairs that are classified as related documents.

Abstract: A method, apparatus, non-transitory computer readable medium, and system of training a domain-specific language model are described. One or more aspects of the method, apparatus, non-transitory computer readable medium, and system include obtaining domain-specific training data including a plurality of domain-specific documents having a document structure corresponding to a domain, and obtaining domain-agnostic training data including a plurality of documents outside of the domain. The domain-specific training data and the domain-agnostic training data are used to train a language model to perform a domain-specific task based on the domain-specific training data and to perform a domain agnostic task based on the domain-agnostic training data.

Abstract: Embodiments of the technology described herein provide a method for generating a unified contract view. The method identifies, within a contract change document, a change instruction for a main contract. The change instruction includes a change introduction and a change content. The method determines an editing intent associated with the change instruction. The method identifies, using the change instruction, a target element in the main contract to be changed. The method generates a unified contract view that depicts the target element modified according to the editing intent and the change content. The method causes the unified contract view to be output for display.

Abstract: Techniques described herein are directed to assisting review of documents. In one embodiment, one or more text segments and one or more subjects in a document are identified. A text segment in the document is associated with a corresponding subject identified in the document. The text segment is classified with a content type value corresponding to a relation of the text segment to the corresponding subject. Thereafter, information is provided for the text segment associated with the corresponding subject for display on a user interface. Such information can include a representation of the content type value for the text segment.