208 research outputs found
Towards a query language for annotation graphs
The multidimensional, heterogeneous, and temporal nature of speech databases
raises interesting challenges for representation and query. Recently,
annotation graphs have been proposed as a general-purpose representational
framework for speech databases. Typical queries on annotation graphs require
path expressions similar to those used in semistructured query languages.
However, the underlying model is rather different from the customary graph
models for semistructured data: the graph is acyclic and unrooted, and both
temporal and inclusion relationships are important. We develop a query language
and describe optimization techniques for an underlying relational
representation.Comment: 8 pages, 10 figure
Emu: Enhancing Multilingual Sentence Embeddings with Semantic Specialization
We present Emu, a system that semantically enhances multilingual sentence
embeddings. Our framework fine-tunes pre-trained multilingual sentence
embeddings using two main components: a semantic classifier and a language
discriminator. The semantic classifier improves the semantic similarity of
related sentences, whereas the language discriminator enhances the
multilinguality of the embeddings via multilingual adversarial training. Our
experimental results based on several language pairs show that our specialized
embeddings outperform the state-of-the-art multilingual sentence embedding
model on the task of cross-lingual intent classification using only monolingual
labeled data.Comment: AAAI 202
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