Provided are an interactive machine translation method and apparatus, a device, and a medium. The method includes: acquiring a source statement input by a user; translating the source statement into a first target statement; determining whether the user adjusts a first vocabulary in the first target statement; and in response to determining that the user adjusts the first vocabulary in the first target statement, acquiring a second vocabulary for replacing the first vocabulary, and adjusting, based on the second vocabulary, a vocabulary sequence located in a front of the first vocabulary and a vocabulary sequence located behind the first vocabulary in the first target statement to generate a second target statement.

Provided are an interactive machine translation method and apparatus, a device, and a medium. The method includes: acquiring a source statement input by a user; translating the source statement into a first target statement; determining whether the user adjusts a first vocabulary in the first target statement; and in response to determining that the user adjusts the first vocabulary in the first target statement, acquiring a second vocabulary for replacing the first vocabulary, and adjusting, based on the second vocabulary, a vocabulary sequence located in a front of the first vocabulary and a vocabulary sequence located behind the first vocabulary in the first target statement to generate a second target statement.

Disclosed are systems and methods for translation comments flowback. In some embodiments, the method includes the steps of: obtaining a first document associated with a primary document, the primary document in a primary language, the first document comprising one or more translated sections in a first language, the one or more translated sections being mapped to one or more sections in the primary document via a content identifier, the first language being different from the primary language; transmitting the first document to a first user for review; receiving a first input associated with the one or more translated sections in the first document from the first user; and populating the first input to the primary document based on the content identifier.

A method and apparatus for training models in machine translation, an electronic device and a storage medium are disclosed, which relates to the field of natural language processing technologies and the field of deep learning technologies. An implementation includes mining similar target sentences of a group of samples based on a parallel corpus using a machine translation model and a semantic similarity model, and creating a first training sample set; training the machine translation model with the first training sample set; mining a negative sample of each sample in the group of samples based on the parallel corpus using the machine translation model and the semantic similarity model, and creating a second training sample set; and training the semantic similarity model with the second training sample set.

A method and apparatus for training models in machine translation, an electronic device and a storage medium are disclosed, which relates to the field of natural language processing technologies and the field of deep learning technologies. An implementation includes mining similar target sentences of a group of samples based on a parallel corpus using a machine translation model and a semantic similarity model, and creating a first training sample set; training the machine translation model with the first training sample set; mining a negative sample of each sample in the group of samples based on the parallel corpus using the machine translation model and the semantic similarity model, and creating a second training sample set; and training the semantic similarity model with the second training sample set.

Systems and methods for automatic evaluation of the quality of NLG outputs. In some aspects of the technology, a learned evaluation model may be pretrained first using NLG model pretraining tasks, and then with further pretraining tasks using automatically generated synthetic sentence pairs. In some cases, following pretraining, the evaluation model may be further fine-tuned using a set of human-graded sentence pairs, so that it learns to approximate the grades allocated by the human evaluators. In some cases, following fine-tuning, the learned evaluation model may be distilled into a student model.

Systems and methods for automatic evaluation of the quality of NLG outputs. In some aspects of the technology, a learned evaluation model may be pretrained first using NLG model pretraining tasks, and then with further pretraining tasks using automatically generated synthetic sentence pairs. In some cases, following pretraining, the evaluation model may be further fine-tuned using a set of human-graded sentence pairs, so that it learns to approximate the grades allocated by the human evaluators. In some cases, following fine-tuning, the learned evaluation model may be distilled into a student model.

Disclosed are a translation method, a classification model training method, a device and a storage medium, which relate to the field of computer technologies, particularly to the field of artificial intelligence such as natural language processing and deep learning. The translation method includes: obtaining a current processing unit of a source language text based on a segmented word in the source language text; determining a classification result of the current processing unit with a classification model; and in response to determining that the classification result is the current processing unit being translatable separately, translating the current processing unit to obtain translation result in a target language corresponding to the current processing unit.

Disclosed are a computer implemented method, system and platform for improving the readability and/or coherency of a conversation transcript, which include the applying of a speech disfluency detection model to identify speech disfluencies in a text transcript and to provide a corrected and/or annotated version of the conversation transcript indicating the edits made vis-à-vis the inputted text transcript.

Disclosed are a computer implemented method, system and platform for improving the readability and/or coherency of a conversation transcript, which include the applying of a speech disfluency detection model to identify speech disfluencies in a text transcript and to provide a corrected and/or annotated version of the conversation transcript indicating the edits made vis-à-vis the inputted text transcript.