[PDF] Top 20 Knowledge Based Semantic Embedding for Machine Translation

... a knowledge base is leveraged to learn an explic- it semantic vector, in which the grounding space is defined by the given knowledge base, then the same knowledge base and a target monolingual ... See full document

... with semantic similarity score In the phrase based MT system we add two fea- tures (semantic similarity scores) to the bilingual phrase ...word embedding to the resultant source ...and ... See full document

... We used OpenNMT (Klein et al., 2017), a generic deep learning framework mainly specialised in sequence-to-sequence models covering a variety of tasks such as machine translation, summarisa- tion, speech ... See full document

... cation on the three datasets. The logical property preserving embeddings in general outperform their base models. lppTransE always shows higher ac- curacy than TransE, lppTransR than TransR except for WN11, and lppTransD ... See full document

... human translation (Padilla & Bajo, ...statistical machine translation to tackle with source-context information in a reliable way has been already recognized as a major drawback of the ... See full document

... erage embedding models for attention-based ...coverage embedding vec- tor for each source word to start with, and keeps up- dating those coverage embeddings with neural net- works as the ... See full document

... Transformer translation systems (Vaswani et ...positional embedding (PE) approach to encode order information into the input ...learned based on the position index of each word and is added to ... See full document

... Another Stride Towards Knowledge Based Machine Translation A n o t h e r St ride T o w a rds K n o w l e d g e B a s e d M a c h i n e T r a n s l a t i o n Masaru Tomita Jaime G Carbonell Computer Sc[.] ... See full document

... The above algorithm is effective only when words linked to an MR predicate and its arguments stay close to each other, a property that we call phrasal coherence. Any links that destroy this property would lead to ... See full document

... explicit semantic model, usually an ontology, that repre- sents the semantics of the target ...notate semantic events and ...using machine translation tools and a CRF, the second as ... See full document

... In order to adapt the semantic processing, we incorporated a Linked Open Data resource (DB- Pedia) in the en ↔ bg experiments via a mapping of the DBpedia ontology to WordNet. Our goal was to use again the IXA ... See full document

... Section 1. Two typical sentence selection methods for PBSMT were also used as baselines: Axelrod et al. (2011) used language model-based cross- entropy difference as criterion; Chen et al. (2016) used a CNN to ... See full document

... of machine translation in use that focus on surface word level suffer from their lack of tolerance of linguistic variance, and the incorporation of linguistic features can improve their ...lexical ... See full document

... reordering knowledge as position- al relations between source and target word- ...SMT based on jump distances between the newly translated phras- es and to-be-translated phrases which does not consider ... See full document

... counts based on the root of the TTS template (Galley et ...algorithm, based only on TTS templates, is slightly better than the baseline ...Adding semantic role features to the EM algorithm actu- ally ... See full document

... Multimodal machine translation is an attrac- tive application of neural machine transla- tion (NMT). It helps computers to deeply understand visual objects and their relations with natural languages. ... See full document

... the Semantic Web with Example Based Ma- chine Translation (EBMT), which is very much related to our ...aid translation of complete sen- ...the Semantic Web standard tooling very ... See full document

... a knowledge-intensive process, which must take into account all variable information about the speech communication process, from acoustics to semantics and ...learning based approach and target language ... See full document

... Recently, embedding-based approaches have shown strong feasibility and ...in knowledge graphs into a continuous, real-valued and low-dimensional vector space, and then make use of the ... See full document

... Using vectors to represent word meanings is the essence of vector space models (VSM). The representations capture words’ semantic and syn- tactic information which can be used to measure semantic ... See full document