48 research outputs found
A Survey of Word Reordering in Statistical Machine Translation: Computational Models and Language Phenomena
Word reordering is one of the most difficult aspects of statistical machine
translation (SMT), and an important factor of its quality and efficiency.
Despite the vast amount of research published to date, the interest of the
community in this problem has not decreased, and no single method appears to be
strongly dominant across language pairs. Instead, the choice of the optimal
approach for a new translation task still seems to be mostly driven by
empirical trials. To orientate the reader in this vast and complex research
area, we present a comprehensive survey of word reordering viewed as a
statistical modeling challenge and as a natural language phenomenon. The survey
describes in detail how word reordering is modeled within different
string-based and tree-based SMT frameworks and as a stand-alone task, including
systematic overviews of the literature in advanced reordering modeling. We then
question why some approaches are more successful than others in different
language pairs. We argue that, besides measuring the amount of reordering, it
is important to understand which kinds of reordering occur in a given language
pair. To this end, we conduct a qualitative analysis of word reordering
phenomena in a diverse sample of language pairs, based on a large collection of
linguistic knowledge. Empirical results in the SMT literature are shown to
support the hypothesis that a few linguistic facts can be very useful to
anticipate the reordering characteristics of a language pair and to select the
SMT framework that best suits them.Comment: 44 pages, to appear in Computational Linguistic
Edinburgh's Statistical Machine Translation Systems for WMT16
This paper describes the University of Edinburgh’s
phrase-based and syntax-based
submissions to the shared translation tasks
of the ACL 2016 First Conference on Machine
Translation (WMT16). We submitted
five phrase-based and five syntaxbased
systems for the news task, plus one
phrase-based system for the biomedical
task
The KIT Translation Systems for IWSLT 2013
In this paper, we present the KIT systems participating in all three official directions, namely English→German, German→English, and English→French, in translation tasks of the IWSLT 2013 machine translation evaluation. Additionally, we present the results for our submissions to the optional directions English→Chinese and English→Arabic. We used phrase-based translation systems to generate the translations. This year, we focused on adapting the systems towards ASR input. Furthermore, we investigated different reordering models as well as an extended discriminative word lexicon. Finally, we added a data selection approach for domain adaptation
Samanantar: The Largest Publicly Available Parallel Corpora Collection for 11 Indic Languages
We present Samanantar, the largest publicly available parallel corpora
collection for Indic languages. The collection contains a total of 49.7 million
sentence pairs between English and 11 Indic languages (from two language
families). Specifically, we compile 12.4 million sentence pairs from existing,
publicly-available parallel corpora, and additionally mine 37.4 million
sentence pairs from the web, resulting in a 4x increase. We mine the parallel
sentences from the web by combining many corpora, tools, and methods: (a)
web-crawled monolingual corpora, (b) document OCR for extracting sentences from
scanned documents, (c) multilingual representation models for aligning
sentences, and (d) approximate nearest neighbor search for searching in a large
collection of sentences. Human evaluation of samples from the newly mined
corpora validate the high quality of the parallel sentences across 11
languages. Further, we extract 83.4 million sentence pairs between all 55 Indic
language pairs from the English-centric parallel corpus using English as the
pivot language. We trained multilingual NMT models spanning all these languages
on Samanantar, which outperform existing models and baselines on publicly
available benchmarks, such as FLORES, establishing the utility of Samanantar.
Our data and models are available publicly at
https://indicnlp.ai4bharat.org/samanantar/ and we hope they will help advance
research in NMT and multilingual NLP for Indic languages.Comment: Accepted to the Transactions of the Association for Computational
Linguistics (TACL
Getting Past the Language Gap: Innovations in Machine Translation
In this chapter, we will be reviewing state of the art machine translation systems, and will discuss innovative methods for machine translation, highlighting the most promising techniques and applications. Machine translation (MT) has benefited from a revitalization in the last 10 years or so, after a period of relatively slow activity. In 2005 the field received a jumpstart when a powerful complete experimental package for building MT systems from scratch became freely available as a result of the unified efforts of the MOSES international consortium. Around the same time, hierarchical methods had been introduced by Chinese researchers, which allowed the introduction and use of syntactic information in translation modeling. Furthermore, the advances in the related field of computational linguistics, making off-the-shelf taggers and parsers readily available, helped give MT an additional boost. Yet there is still more progress to be made. For example, MT will be enhanced greatly when both syntax and semantics are on board: this still presents a major challenge though many advanced research groups are currently pursuing ways to meet this challenge head-on. The next generation of MT will consist of a collection of hybrid systems. It also augurs well for the mobile environment, as we look forward to more advanced and improved technologies that enable the working of Speech-To-Speech machine translation on hand-held devices, i.e. speech recognition and speech synthesis. We review all of these developments and point out in the final section some of the most promising research avenues for the future of MT
Proceedings of the 17th Annual Conference of the European Association for Machine Translation
Proceedings of the 17th Annual Conference of the European Association for Machine Translation (EAMT
Computational models for semantic textual similarity
164 p.The overarching goal of this thesis is to advance on computational models of meaning and their evaluation. To achieve this goal we define two tasks and develop state-of-the-art systems that tackle both task: Semantic Textual Similarity (STS) and Typed Similarity.STS aims to measure the degree of semantic equivalence between two sentences by assigning graded similarity values that capture the intermediate shades of similarity. We have collected pairs of sentences to construct datasets for STS, a total of 15,436 pairs of sentences, being by far the largest collection of data for STS.We have designed, constructed and evaluated a new approach to combine knowledge-based and corpus-based methods using a cube. This new system for STS is on par with state-of-the-art approaches that make use of Machine Learning (ML) without using any of it, but ML can be used on this system, improving the results.Typed Similarity tries to identify the type of relation that holds between a pair of similar items in a digital library. Providing a reason why items are similar has applications in recommendation, personalization, and search. A range of types of similarity in this collection were identified and a set of 1,500 pairs of items from the collection were annotated using crowdsourcing.Finally, we present systems capable of resolving the Typed Similarity task. The best system resulted in a real-world application to recommend similar items to users in an online digital library