The NTU-MC compilation taps on the linguistic diversity of multilingual texts available within Singapore. The current version of NTU-MC contains 375,000 words (15,000 sentences) in 6 languages (English, Chinese, Japanese, Korean, Indonesian and Vietnamese) from 6 language families (Indo-European, Sino-Tibetan, Japonic, Korean as a language isolate, Austronesian and Austro-Asiatic). The NTU-MC i...

In this paper we give an overview of the Tri-lingual Entity Discovery and Linking (EDL) task at the Knowledge Base Population (KBP) track at TAC2017, and of the Ten Low Resource Language EDL Pilot. We will summarize several new and effective research directions including multi-lingual common space construction for cross-lingual knowledge transfer, rapid approaches for silver-standard training d...

Current approaches to cross-lingual sentiment analysis try to leverage the wealth of labeled English data using bilingual lexicons, bilingual vector space embeddings, or machine translation systems. Here we show that it is possible to use a single linear transformation, with as few as 2000 word pairs, to capture fine-grained sentiment relationships between words in a cross-lingual setting. We a...

A cross-lingual voice conversion system aims at modifying the timbral structure of recorded sentences from a source speaker, in order to obtain processed sentences which are perceived as the same sentences uttered by a target speaker. This work presents the cross-lingual voice conversion problem as a network of related sub-problems and discuss several techniques for solving each of these sub-pr...

We present ATTRACT-REPEL, an algorithm for improving the semantic quality of word vectors by injecting constraints extracted from lexical resources. ATTRACT-REPEL facilitates the use of constraints from monoand crosslingual resources, yielding semantically specialized cross-lingual vector spaces. Our evaluation shows that the method can make use of existing cross-lingual lexicons to construct h...

We introduce XMEANT—a new cross-lingual version of the semantic frame based MT evaluation metric MEANT—which can correlate even more closely with human adequacy judgments than monolingual MEANT and eliminates the need for expensive human references. Previous work established that MEANT reflects translation adequacy with state-of-the-art accuracy, and optimizing MT systems against MEANT robustly...

Political text scaling aims to linearly order parties and politicians across political dimensions (e.g., left-to-right ideology) based on textual content (e.g., politician speeches or party manifestos). Existing models scale texts based on relative word usage and cannot be used for cross-lingual analyses. Additionally, there is little quantitative evidence that the output of these models correl...

We develop a novel cross-lingual word representation model which injects syntactic information through dependencybased contexts into a shared cross-lingual word vector space. The model, termed CLDEPEMB, is based on the following assumptions: (1) dependency relations are largely language-independent, at least for related languages and prominent dependency links such as direct objects, as evidenc...

Cross-lingual tasks are especially difficult due to the compounding effect of errors in language processing and errors in machine translation (MT). In this paper, we present an error analysis of a new cross-lingual task: the 5W task, a sentence-level understanding task which seeks to return the English 5W's (Who, What, When, Where and Why) corresponding to a Chinese sentence. We analyze systems...

In the EMIME project, we are developing a mobile device that performs personalized speech-to-speech translation such that a user’s spoken input in one language is used to produce spoken output in another language, while continuing to sound like the user’s voice. We integrate two techniques, unsupervised adaptation for HMM-based TTS using a wordbased large-vocabulary continuous speech recognizer...