BLISS in Non-Isometric Embedding Spaces
Recent work on bilingual lexicon induction (BLI) has frequently depended either on aligned bilingual lexicons or on distribution matching, often with an assumption about the isometry of the two spaces. We propose a technique to quantitatively estimate this assumption of the isometry between two embedding spaces and empirically show that this assumption weakens as the languages in question become increasingly etymologically distant. We then propose Bilingual Lexicon Induction with Semi-Supervision (BLISS) --- a novel semi-supervised approach that relaxes the isometric assumption while leveraging both limited aligned bilingual lexicons and a larger set of unaligned word embeddings, as well as a novel hubness filtering technique. Our proposed method improves over strong baselines for 11 of 14 on the MUSE dataset, particularly for languages whose embedding spaces do not appear to be isometric. In addition, we also show that adding supervision stabilizes the learning procedure, and is effective even with minimal supervision.
Code (0)
등록된 구현이 없습니다.
Tasks
Bilingual Lexicon InductionWord EmbeddingsSimilar Papers 제목 키워드 기반
Bilingual Lexicon Induction with Semi-supervision in Non-Isometric Embedding Spaces
Recent work on bilingual lexicon induction (BLI) has frequently depended either on aligned bilingual lexicons or on distribution matching, often with an assumption about the isometry of the two spaces. We propose a techn…
Bilingual Lexicon InductionWord EmbeddingsHilbert Exclusion: Improved Metric Search through Finite Isometric Embeddings
Most research into similarity search in metric spaces relies upon the triangle inequality property. This property allows the space to be arranged according to relative distances to avoid searching some subspaces. We show…
The Gaussian kernel on the circle and spaces that admit isometric embeddings of the circle
On Euclidean spaces, the Gaussian kernel is one of the most widely used kernels in applications. It has also been used on non-Euclidean spaces, where it is known that there may be (and often are) scale parameters for whi…
Rate-Distortion Optimization Guided Autoencoder for Isometric Embedding in Euclidean Latent Space
To analyze high-dimensional and complex data in the real world, deep generative models, such as variational autoencoder (VAE) embed data in a low-dimensional space (latent space) and learn a probabilistic model in the la…
Anomaly DetectionRelationUnsupervised Anomaly DetectionBLiSS: Bootstrapped Linear Shape Space
Morphable models are fundamental to numerous human-centered processes as they offer a simple yet expressive shape space. Creating such morphable models, however, is both tedious and expensive. The main challenge is estab…