We describe the Sogou neural machine translation systems for the WMT 2017 Chinese↔English news translation tasks. Our systems are based on a multilayer encoder-decoder architecture with attention mechanism. The best translation is obtained with ensemble and reranking techniques. We also propose an approach to improve the named entity translation problem. Our Chinese→English system achieved the ...

In this report, we describe our (NEUNLPLab) phrase-based statistical machine translation (SMT) system (NEUTrans) for the participation of news domain Chinese-to-English single-system translation task in the 5 China workshop on Machine Translation (CWMT2009). We submitted four translation results for this task. In this report, we first give an introduction of the framework and the key techniques...

This paper describes the Neural Machine Translation systems of Xiamen University for the translation tasks of WMT 17. Our systems are based on the Encoder-Decoder framework with attention. We participated in three directions of shared news translation tasks: English→German and Chinese↔English. We experimented with deep architectures, different segmentation models, synthetic training data and ta...

Informedia Digital Video Library Alexander G. Hauptmann, Danny Lee and Paul E. Kennedy Abstract The Informedia Digital Video Library Project includes a multilingual component for retrieval of video documents in multiple languages and a topic-labeling component for English video documents. We now extend this capability to English topic labeling of foreign-language broadcast-news stories. News st...

We propose methods to exploit contemporary news articles in a resource rich language, together with cross-language information retrieval and machine translation, to sharpen language models for a news story in a language with fewer linguistic resources. We report experimental results on storyspecific Chinese language models that use cues from a parallel corpus of English news stories. We demonst...

An increasing amount of news content is produced in audiovideo form every day. To effectively analyse and monitoring this multilingual data stream, we require methods to extract and present audio content in accessible ways. In this paper, we describe an end-to-end system for processing and browsing audio news data. This fully automated system brings together our recent research on audio scene a...

Stemming is an important analysis step in a number of areas such as natural language processing (NLP), information retrieval (IR), machine translation(MT) and text classification. In this paper we present the development of a stemmer for Amharic that reduces words to their citation forms. Amharic is a Semitic language with rich and complex morphology. The application of such a stemmer is in dic...

We have developed an approach for topic detection from multilingual news, in particular Chinese and English. We extract named entities such as people names, geographical location names, and organization names automatically from the news content by transformation-based linguistic taggers. These sets of named entities together with the remaining content terms form the basis of news representation...

We propose methods to exploit contemporary news articles in a resource rich language, together with cross-language information retrieval and machine translation, to sharpen language models for a news story in a language with fewer linguistic resources. We report experimental results on storyspecific Chinese language models that use cues from a parallel corpus of English news stories. We demonst...