3 results about "Sentence length" patented technology

This invention discloses an edge AI dialogue interaction adaptation and inference optimization method for children's smart hardware, specifically involving the field of edge AI dialogue interaction adaptation and inference optimization technology. The method includes acquiring the current round of child speech, corresponding text, user age information, historical interaction sequences, remaining memory, processor usage, remaining battery power, and network connectivity status at the edge computing terminal; performing child semantic normalization on the current round of child speech and corresponding text to extract sentence length, word difficulty, intent category, emotion category, and context depth to form a joint state value; mapping the child's age adaptation results, semantic carrying requirements, and edge terminal resource status together into an age semantic resource state graph; and performing local inference and out-of-bounds backoff after filtering and perturbing the target inference trajectory in this state graph.

This application provides a subtitle line-level alignment method, electronic device, and storage medium, including comparing the number of sentences in two text segments; intelligently segmenting the target language text according to its linguistic habits; using a pre-trained large-scale bilingual semantic embedding model to convert each sentence of the source and target language texts into a high-dimensional semantic vector; calculating the semantic similarity matrix between all sentence pairs and finding an alignment path with optimal semantic similarity globally; introducing a length ratio penalty factor to balance semantic similarity and sentence length reasonableness; if blank lines exist, locating the text line that produces the blank line and its adjacent areas, segmenting the preceding sentence of the target language text according to common punctuation marks of that language to organize sentence fragments and eliminate blank lines. This method adopts a "coarse-to-fine, progressively layered" processing strategy, ultimately outputting an alignment result without blank lines and with reasonable semantic matching.

A computer-implemented method includes obtaining, from text corpus including article-summary pairs in a plurality of languages, a plurality of article-summary pairs in a target language among the plurality of languages, to form an article-summary pairs dataset in which each article corresponds to a summary; inputting articles from the article-summary pairs to a machine learning model; generating, by the machine learning model, embeddings for sentences of the articles; extracting, by the machine learning model, keywords from the articles with a probability that varies based on lengths of the sentences, respectively; outputting, by the machine learning model, the keywords; applying a maximal marginal relevance algorithm to the extracted keywords, to select relevant keywords; and generating a keyword-text pairs dataset that includes the relevant keywords and text from the articles, the text corresponding to the relevant keywords in each of keyword-text pairs of the keyword-text pairs dataset.