328,319 research outputs found
Knowledge-based disambiguation for machine translation
The resolution of ambiguities is one of the central problems for Machine Translation. In this paper we propose a knowledge-based approach to disambiguation which uses Description Logics (DL) as representation formalism. We present the process of anaphora resolution implemented in the Machine Translation system FAST and show how the DL system BACK is used to support disambiguation. The disambiguation strategy uses factors representing syntactic, semantic, and conceptual constraints with different weights to choose the most adequate antecedent candidate. We show how these factors can be declaratively represented as defaults in BACK. Disambiguation is then achieved by determining the interpretation that yields a qualitatively minimal number of exceptions to the defaults, and can thus be formalized as exception minimization
Using Cross-Lingual Explicit Semantic Analysis for Improving Ontology Translation
Semantic Web aims to allow machines to make inferences using the explicit conceptualisations contained in ontologies. By pointing to ontologies, Semantic Web-based applications are able to inter-operate and share common information easily. Nevertheless, multilingual semantic applications are still rare, owing to the fact that most online ontologies are monolingual in English. In order to solve this issue, techniques for ontology localisation and translation are needed. However, traditional machine translation is difficult to apply to ontologies, owing to the fact that ontology labels tend to be quite short in length and linguistically different from the free text paradigm. In this paper, we propose an approach to enhance machine translation of ontologies based on exploiting the well-structured concept descriptions contained in the ontology. In particular, our approach leverages the semantics contained in the ontology by using Cross Lingual Explicit Semantic Analysis (CLESA) for context-based disambiguation in phrase-based Statistical Machine Translation (SMT). The presented work is novel in the sense that application of CLESA in SMT has not been performed earlier to the best of our knowledge
Korean to English Translation Using Synchronous TAGs
It is often argued that accurate machine translation requires reference to
contextual knowledge for the correct treatment of linguistic phenomena such as
dropped arguments and accurate lexical selection. One of the historical
arguments in favor of the interlingua approach has been that, since it revolves
around a deep semantic representation, it is better able to handle the types of
linguistic phenomena that are seen as requiring a knowledge-based approach. In
this paper we present an alternative approach, exemplified by a prototype
system for machine translation of English and Korean which is implemented in
Synchronous TAGs. This approach is essentially transfer based, and uses
semantic feature unification for accurate lexical selection of polysemous
verbs. The same semantic features, when combined with a discourse model which
stores previously mentioned entities, can also be used for the recovery of
topicalized arguments. In this paper we concentrate on the translation of
Korean to English.Comment: ps file. 8 page
Hand in hand: automatic sign Language to English translation
In this paper, we describe the first data-driven automatic sign-language-to- speech translation system. While both sign language (SL) recognition and translation techniques exist, both use an intermediate notation system
not directly intelligible for untrained users. We combine a SL recognizing framework with a state-of-the-art phrase-based machine translation (MT) system, using corpora of both American Sign Language and Irish Sign Language
data. In a set of experiments we show the overall results and also illustrate the importance of including a
vision-based knowledge source in the development of a complete SL translation system
Bootstrapping Multilingual Intent Models via Machine Translation for Dialog Automation
With the resurgence of chat-based dialog systems in consumer and enterprise
applications, there has been much success in developing data-driven and
rule-based natural language models to understand human intent. Since these
models require large amounts of data and in-domain knowledge, expanding an
equivalent service into new markets is disrupted by language barriers that
inhibit dialog automation.
This paper presents a user study to evaluate the utility of out-of-the-box
machine translation technology to (1) rapidly bootstrap multilingual spoken
dialog systems and (2) enable existing human analysts to understand foreign
language utterances. We additionally evaluate the utility of machine
translation in human assisted environments, where a portion of the traffic is
processed by analysts. In English->Spanish experiments, we observe a high
potential for dialog automation, as well as the potential for human analysts to
process foreign language utterances with high accuracy.Comment: 6 pages, 3 figures, accepted for publication at the 2018 European
Association for Machine Translation Conference (EAMT 2018
Cross-lingual Entity Alignment via Joint Attribute-Preserving Embedding
Entity alignment is the task of finding entities in two knowledge bases (KBs)
that represent the same real-world object. When facing KBs in different natural
languages, conventional cross-lingual entity alignment methods rely on machine
translation to eliminate the language barriers. These approaches often suffer
from the uneven quality of translations between languages. While recent
embedding-based techniques encode entities and relationships in KBs and do not
need machine translation for cross-lingual entity alignment, a significant
number of attributes remain largely unexplored. In this paper, we propose a
joint attribute-preserving embedding model for cross-lingual entity alignment.
It jointly embeds the structures of two KBs into a unified vector space and
further refines it by leveraging attribute correlations in the KBs. Our
experimental results on real-world datasets show that this approach
significantly outperforms the state-of-the-art embedding approaches for
cross-lingual entity alignment and could be complemented with methods based on
machine translation
Memory-augmented Neural Machine Translation
Neural machine translation (NMT) has achieved notable success in recent
times, however it is also widely recognized that this approach has limitations
with handling infrequent words and word pairs. This paper presents a novel
memory-augmented NMT (M-NMT) architecture, which stores knowledge about how
words (usually infrequently encountered ones) should be translated in a memory
and then utilizes them to assist the neural model. We use this memory mechanism
to combine the knowledge learned from a conventional statistical machine
translation system and the rules learned by an NMT system, and also propose a
solution for out-of-vocabulary (OOV) words based on this framework. Our
experiments on two Chinese-English translation tasks demonstrated that the
M-NMT architecture outperformed the NMT baseline by and BLEU points
on the two tasks, respectively. Additionally, we found this architecture
resulted in a much more effective OOV treatment compared to competitive
methods
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