Bilingual Terminology Extraction from Non-Parallel E-Commerce Corpora

Bilingual terminologies are important resources for natural language processing (NLP) applications. The acquisition of bilingual terminology pairs is either human translation or automatic extraction from parallel data. We notice that comparable corpora could also be a good resource for extracting bilingual terminology pairs, especially for e-commerce domain. The parallel corpora are particularly scarce in e-commerce settings, but the non-parallel corpora in different languages from the same domain are easily available. In this paper, we propose a novel framework of extracting bilingual terminologies from non-parallel comparable corpus in e-commerce. Benefiting from cross-lingual pre-training in e-commerce, our framework can extract the corresponding target terminology by fully utilizing the deep semantic relationship between source-side terminology and target-side sentence. Experimental results on various language pairs show that our approaches achieve significantly better performance than various strong baselines.

A Generalized Language Model in Tensor Space

In the literature, tensors have been effectively used for capturing the context information in language models. However, the existing methods usually adopt relatively-low order tensors, which have limited expressive power in modeling language. Developing a higher-order tensor representation is challenging, in terms of deriving an effective solution and showing its generality. In this paper, we propose a language model named Tensor Space Language Model (TSLM), by utilizing tensor networks and tensor decomposition. In TSLM, we build a high-dimensional semantic space constructed by the tensor product of word vectors. Theoretically, we prove that such tensor representation is a generalization of the n-gram language model. We further show that this high-order tensor representation can be decomposed to a recursive calculation of conditional probability for language modeling. The experimental results on Penn Tree Bank (PTB) dataset and WikiText benchmark demonstrate the effectiveness of TSLM.