918 research outputs found
Filling Knowledge Gaps in a Broad-Coverage Machine Translation System
Knowledge-based machine translation (KBMT) techniques yield high quality in
domains with detailed semantic models, limited vocabulary, and controlled input
grammar. Scaling up along these dimensions means acquiring large knowledge
resources. It also means behaving reasonably when definitive knowledge is not
yet available. This paper describes how we can fill various KBMT knowledge
gaps, often using robust statistical techniques. We describe quantitative and
qualitative results from JAPANGLOSS, a broad-coverage Japanese-English MT
system.Comment: 7 pages, Compressed and uuencoded postscript. To appear: IJCAI-9
A Correlational Encoder Decoder Architecture for Pivot Based Sequence Generation
Interlingua based Machine Translation (MT) aims to encode multiple languages
into a common linguistic representation and then decode sentences in multiple
target languages from this representation. In this work we explore this idea in
the context of neural encoder decoder architectures, albeit on a smaller scale
and without MT as the end goal. Specifically, we consider the case of three
languages or modalities X, Z and Y wherein we are interested in generating
sequences in Y starting from information available in X. However, there is no
parallel training data available between X and Y but, training data is
available between X & Z and Z & Y (as is often the case in many real world
applications). Z thus acts as a pivot/bridge. An obvious solution, which is
perhaps less elegant but works very well in practice is to train a two stage
model which first converts from X to Z and then from Z to Y. Instead we explore
an interlingua inspired solution which jointly learns to do the following (i)
encode X and Z to a common representation and (ii) decode Y from this common
representation. We evaluate our model on two tasks: (i) bridge transliteration
and (ii) bridge captioning. We report promising results in both these
applications and believe that this is a right step towards truly interlingua
inspired encoder decoder architectures.Comment: 10 page
Transitive probabilistic CLIR models.
Transitive translation could be a useful technique to enlarge the number of supported language pairs for a cross-language information retrieval (CLIR) system in a cost-effective manner. The paper describes several setups for transitive translation based on probabilistic translation models. The transitive CLIR models were evaluated on the CLEF test collection and yielded a retrieval effectiveness\ud
up to 83% of monolingual performance, which is significantly better than a baseline using the synonym operator
Abstract syntax as interlingua: Scaling up the grammatical framework from controlled languages to robust pipelines
Syntax is an interlingual representation used in compilers. Grammatical Framework (GF) applies the abstract syntax idea to natural languages. The development of GF started in 1998, first as a tool for controlled language implementations, where it has gained an established position in both academic and commercial projects. GF provides grammar resources for over 40 languages, enabling accurate generation and translation, as well as grammar engineering tools and components for mobile and Web applications. On the research side, the focus in the last ten years has been on scaling up GF to wide-coverage language processing. The concept of abstract syntax offers a unified view on many other approaches: Universal Dependencies, WordNets, FrameNets, Construction Grammars, and Abstract Meaning Representations. This makes it possible for GF to utilize data from the other approaches and to build robust pipelines. In return, GF can contribute to data-driven approaches by methods to transfer resources from one language to others, to augment data by rule-based generation, to check the consistency of hand-annotated corpora, and to pipe analyses into high-precision semantic back ends. This article gives an overview of the use of abstract syntax as interlingua through both established and emerging NLP applications involving GF
Robust Subgraph Generation Improves Abstract Meaning Representation Parsing
The Abstract Meaning Representation (AMR) is a representation for open-domain
rich semantics, with potential use in fields like event extraction and machine
translation. Node generation, typically done using a simple dictionary lookup,
is currently an important limiting factor in AMR parsing. We propose a small
set of actions that derive AMR subgraphs by transformations on spans of text,
which allows for more robust learning of this stage. Our set of construction
actions generalize better than the previous approach, and can be learned with a
simple classifier. We improve on the previous state-of-the-art result for AMR
parsing, boosting end-to-end performance by 3 F on both the LDC2013E117 and
LDC2014T12 datasets.Comment: To appear in ACL 201
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