A large percentage of the world’s population speaks a language of the Indian subcontinent, what we will call here Indic languages, comprising languages from both Indo-European (e.g., Hindi, Bangla, Gujarati, etc.) and Dravidian (e.g., Tamil, Telugu, Malayalam, etc.) families, upwards of 1.5 Billion people. A universal characteristic of Indic languages is their complex morphology, which, when combined with the general lack of sufficient quantities of high quality parallel data, can make developing machine translation (MT) for these languages difficult. In this paper, we describe our efforts towards developing general domain English–Bangla MT systems which are deployable to the Web. We initially developed and deployed SMT-based systems, but over time migrated to NMT-based systems. Our initial SMT-based systems had reasonably good BLEU scores, however, using NMT systems, we have gained significant improvement over SMT baselines. This is achieved using a number of ideas to boost the data store and counter data sparsity: crowd translation of intelligently selected monolingual data (throughput enhanced by an IME (Input Method Editor) designed specifically for QWERTY keyboard entry for Devanagari scripted languages), back-translation, different regularization techniques, dataset augmentation and early stopping.

We describe the Microsoft Speech Language Translation (MSLT) corpus, which was created in order to evaluate end-to-end conversational speech translation quality. The corpus was created from actual conversations over Skype, and we provide details on the recording setup and the different layers of associated text data. The corpus release includes Test and Dev sets with reference transcripts for speech recognition. Additionally, cleaned up transcripts and reference translations are available for evaluation of machine translation quality. The IWSLT 2016 release described here includes the source audio, raw transcripts, cleaned up transcripts, and translations to or from English for both French and German.

In this paper, we describe the expansion of the ODIN resource, a database containing many thousands of instances of Interlinear Glossed Text (IGT) for over a thousand languages harvested from scholarly linguistic papers posted to the Web. A database containing a large number of instances of IGT, which are effectively richly annotated and heuristically aligned bitexts, provides a unique resource for bootstrapping NLP tools for resource-poor languages. To make the data in ODIN more readily consumable by tool developers and NLP researchers, we propose a new XML format for IGT, called Xigt. We call the updated release ODIN-II.

We broaden the application of data selection methods for domain adaptation to a larger number of languages, data, and decoders than shown in previous work, and explore comparable applications for both monolingual and bilingual cross-entropy difference methods. We compare domain adapted systems against very large general-purpose systems for the same languages, and do so without a bias to a particular direction. We present results against real-world generalpurpose systems tuned on domain-specific data, which are substantially harder to beat than standard research baseline systems. We show better performance for nearly all domain adapted systems, despite the fact that the domainadapted systems are trained on a fraction of the content of their general domain counterparts. The high performance of these methods suggest applicability to a wide variety of contexts, particularly in scenarios where only small supplies of unambiguously domain-specific data are available, yet it is believed that additional similar data is included in larger heterogenous-content general-domain corpora.

Syntactic parses can provide valuable information for many NLP tasks, such as machine translation, semantic analysis, etc. However, most of the world's languages do not have large amounts of syntactically annotated corpora available for building parsers. Syntactic projection techniques attempt to address this issue by using parallel corpora between resource-poor and resource-rich languages, bootstrapping the resource-poor language with the syntactic analysis of the resource-rich language. In this paper, we investigate the possibility of using small, parallel, annotated corpora to automatically detect divergent structural patterns between two languages. These patterns can then be used to improve structural projection algorithms, allowing for better performing NLP tools for resource-poor languages, in particular those that may not have large amounts of annotated data necessary for traditional, fully-supervised methods. While this detection process is not exhaustive, we demonstrate that important instances of divergence are picked up with minimal prior knowledge of a given language pair.

In this paper, we discuss the development of statistical machine translation for English to/from White Hmong (Language code: mww). White Hmong is a Hmong-Mien language, originally spoken mostly in Southeast Asia, but now predominantly spoken by a large diaspora throughout the world, with populations in the United States, Australia, France, Thailand and elsewhere. Building statistical translation systems for Hmong proved to be incredibly challenging since there are no known parallel or monolingual corpora for the language; in fact, finding data for Hmong proved to be one of the biggest challenges to getting the project off the ground. It was only through a close collaboration with the Hmong community, and active and tireless participation of Hmong speakers, that it became possible to build up a critical mass of data to make the translation project a reality. We see this effort as potentially replicable for other severely resource poor languages of the world, which is likely the case for the majority of the languages still spoken on the planet. Further, the work here suggests that research and work on other severely under-resourced languages can have significant positive impacts for the affected communities, both for accessibility and language preservation.

We examine pooling data as a method for improving Statistical Machine Translation (SMT) quality for narrowly defined domains, such as data for a particular company or public entity. By pooling all available data, building large SMT engines, and using domain-specific target language models, we see boosts in quality, and can achieve the generalizability and resiliency of a larger SMT but with the precision of a domain-specific engine.

As the data for more and more languages is finding its way into digital form, with an increasing amount of this data being posted to the Web, it has become possible to collect language data from the Web and create large multilingual resources, covering hundreds or even thousands of languages. ODIN, the Online Database of INterlinear text (Lewis, 2006), is such a resource. It currently consists of nearly 200,000 data points for over 1,000 languages, the data for which was harvested from linguistic documents on the Web. We identify a number of issues with language identification for such broad-coverage resources including the lack of training data, ambiguous language names, incomplete language code sets, and incorrect uses of language names and codes. After providing a short overview of existing language code sets maintained by the linguistic community, we discuss what linguists and the linguistic community can do to make the process of language identification easier.