Augmenting a Statistical Translation System with a Translation Memory

In this paper, we present a translation memory (TM) based system to augment a statistical translation (SMT) system. It is used for translating sentences which have close matches in the training corpus. Given a test sentence, we first extract sentence pairs from the training corpus, whose source side is similar to the test sentence. Then, the TM system modifies the translation of the sentences by a sequence of substitution, deletion and insertion operations, to obtain the desired result. Statistical phrase alignment model of the SMT system is used for this purpose. The system was evaluated using a corpus of Chinese-English conversational data. For close matching sentences, the translations produced by the translation memory approach were compared with the translations of the statistical decoder

ITEm: Unsupervised Image-Text Embedding Learning for eCommerce

Product embedding serves as a cornerstone for a wide range of applications in eCommerce. The product embedding learned from multiple modalities shows significant improvement over that from a single modality, since different modalities provide complementary information. However, some modalities are more informatively dominant than others. How to teach a model to learn embedding from different modalities without neglecting information from the less dominant modality is challenging. We present an image-text embedding model (ITEm), an unsupervised learning method that is designed to better attend to image and text modalities. We extend BERT by (1) learning an embedding from text and image without knowing the regions of interest; (2) training a global representation to predict masked words and to construct masked image patches without their individual representations. We evaluate the pre-trained ITEm on two tasks: the search for extremely similar products and the prediction of product categories, showing substantial gains compared to strong baseline models

Recent improvements in the CMU large-scale Chinese-English SMT system

In this paper we describe recent improvements to components and methods used in our statistical machine translation system for Chinese-English used in the January 2008 GALE evaluation. Main improvements are results of consistent data processing, larger statistical models and a POS-based word reordering approach

Thai Grapheme-Based Speech Recognition

In this paper we present the results for building a grapheme-based speech recognition system for Thai. We experiment with different settings for the initial context independent system, different number of acoustic models and different contexts for the speech unit. In addition, we investigate the potential of an enhanced tree clustering method as a way of sharing parameters across models. We compare our system with two phoneme-based systems; one that uses a hand-crafted dictionary and another that uses an automatically generated dictionary. Experiment results show that the grapheme-based system with enhanced tree clustering outperforms the phoneme-based system using an automatically generated dictionary, and has comparable results to the phoneme-based system with the handcrafted dictionary.

Thai Grapheme-Based Speech Recognition

In this paper we present the results for building a grapheme-based speech recognition system for Thai. We experiment with different settings for the initial context independent system, different number of acoustic models and different contexts for the speech unit. In addition, we investigate the potential of an enhanced tree clustering method as a way of sharing parameters across models. We compare our system with two phoneme-based systems; one that uses a hand-crafted dictionary and another that uses an automatically generated dictionary