An example method involves (i) accessing first data defining multiple portions of a content item, wherein at least a plurality of the portions represent text; (ii) selecting, from the plurality of portions representing text, a subset of the portions representing text, wherein the selecting is based on each portion of the selected subset having a particular characteristic; (iii) based on the text represented by the portions of the selected subset, generating second data that represents a concatenation of the text represented by the portions of the selected subset; and (iv) providing output based on the generated second data.

A method for automatic presentation of a terminal application screen is described. The method includes receiving terminal application screen data from a server. The method also includes selecting a transformation template based on a comparison of text in the terminal application screen data to identification text in the transformation template. The transformation template includes instructions for transforming the terminal application screen data into an HTML page. The method further includes transforming the terminal application screen data into the HTML page using the selected transformation template. The HTML page is displayed in a web interface on a client device.

A method for automatic presentation of a terminal application screen is described. The method includes receiving terminal application screen data from a server. The method also includes selecting a transformation template based on a comparison of text in the terminal application screen data to identification text in the transformation template. The transformation template includes instructions for transforming the terminal application screen data into an HTML page. The method further includes transforming the terminal application screen data into the HTML page using the selected transformation template. The HTML page is displayed in a web interface on a client device.

Embodiments are directed to translating a natural language query into a code snippet in a programing language that semantically represents the query. The embodiments include a cascading neural network that includes an encoder network and a classifier network. The encoder network being faster but less accurate than the classifier network. The encoder network is trained using a contrastive learning framework to identify code candidates from a large set of code snippets. The classifier network is trained using a binary classifier to identify the code snippet that semantically represents the query from the code candidates.

Embodiments are directed to translating a natural language query into a code snippet in a programing language that semantically represents the query. The embodiments include a cascading neural network that includes an encoder network and a classifier network. The encoder network being faster but less accurate than the classifier network. The encoder network is trained using a contrastive learning framework to identify code candidates from a large set of code snippets. The classifier network is trained using a binary classifier to identify the code snippet that semantically represents the query from the code candidates.

A client device processing received emoji messages using emoji-first messaging. Text messaging is automatically converted to emojis by an emoji-first application so that only emojis are communicated from one client device to another client device. Each client device has a library of emojis that are mapped to words, which libraries are customizable and unique to the users of the client devices, such that the users can communicate secretly in code. Upon receipt of a string of emojis, a user can select the emoji string to convert to text if desired, for a predetermined period of time.

The present disclosure provides a model training method and apparatus, an electronic device, and a storage medium, and relates to the field of artificial intelligence, in particular, to the field of natural language processing and deep learning. A specific implementation solution includes: constructing initial training corpora; performing data enhancement on the initial training corpora based on an algorithm contained in a target algorithm set to obtain target training corpora, wherein the target algorithm set is determined from multiple algorithm sets, and different algorithm sets are used for performing data enhancement on corpora with different granularity in the initial training corpora; and performing training on a language model based on the target training corpora to obtain a sequence labeling model, herein the language model is pre-trained based on text corpora.

The present disclosure provides a model training method and apparatus, an electronic device, and a storage medium, and relates to the field of artificial intelligence, in particular, to the field of natural language processing and deep learning. A specific implementation solution includes: constructing initial training corpora; performing data enhancement on the initial training corpora based on an algorithm contained in a target algorithm set to obtain target training corpora, wherein the target algorithm set is determined from multiple algorithm sets, and different algorithm sets are used for performing data enhancement on corpora with different granularity in the initial training corpora; and performing training on a language model based on the target training corpora to obtain a sequence labeling model, herein the language model is pre-trained based on text corpora.

The disclosed technology is generally directed to the conversion of text to tables. In one example of the technology, input text that includes at least three rows is received. A plurality of characteristics of the input text is determined. Each characteristic of the plurality of characteristics is associated with a uniformity between the rows of the input text. The plurality of characteristics includes at least one characteristic that is associated with a delimiter count. A determination is made as to whether the input text is suitable for conversion to table based on the plurality of characteristics. Upon determining that the input text is suitable for conversion to a table, a machine learning model is used to convert the input text into a table.

A system and method are presented for interoperability of computer software applications. Subdocuments are located within slots of a main document along with a fully-qualified file identifier. Subdocuments can be saved in the main document in an intermediate format. Editing a slot triggers the opening of the original document in its native application. An intermediate format file is then created and reimported into its original slot. A document containing subdocuments can be exported in its entirety from one program to another. An auxiliary program identifies a target template format and converts the original document to the target template in its native application using the original subdocuments. Page image exports are used to transfer all content in the original application document to a destination document in a different format.