[PDF] Top 20 Predicting Machine Translation Adequacy with Document Embeddings
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Predicting Machine Translation Adequacy with Document Embeddings
... of Translation with Explicit ORdering) (Denkowski and Lavie, 2014) is an MT evaluation metric which tries to consider both grammatical and semantic knowl- ...hypothesis translation and a reference ... See full document
9
Exploring the use of Acoustic Embeddings in Neural Machine Translation
... frequency-inverse document frequency (TF-IDF) vectors that are computed for each document of the training text data, which are then used to train LDA ...each document, where a document ... See full document
9
Enriching Phrase Tables for Statistical Machine Translation Using Mixed Embeddings
... bilingual embeddings was proposed in Mikolov et ...into embeddings, then try to find a transformation function to map the source embedding space into the target ...word-level translation engine with ... See full document
10
Equalizing Gender Bias in Neural Machine Translation with Word Embeddings Techniques
... the translation also depends on the professions from the Occupations test and its predicted ...lem predicting the gender of professions in the context of “him”, so we focus the analysis on the context of ... See full document
8
Extending Machine Translation Evaluation Metrics with Lexical Cohesion to Document Level
... the document and system ...their adequacy assessments are used as evaluation data. Note that the adequacy assessment is in fact an evaluation method for the sentence ...emulate document-level ... See full document
9
Bilingual Word Embeddings for Phrase Based Machine Translation
... Distributed word representations are useful in NLP applications such as information retrieval (Pas¸ca et al., 2006; Manning et al., 2008), search query ex- pansions (Jones et al., 2006), or representing se- mantics of ... See full document
6
The FAUST Corpus of Adequacy Assessments for Real-World Machine Translation Output
... The translation requests, collected through the popular translation portal ...real-world machine translation (MT) usage, from complete sentences to units of one or two words, from well-formed ... See full document
7
Lexical Chains meet Word Embeddings in Document level Statistical Machine Translation
... the translation of a phrase with another from the phrase table), swap-phrases (ex- changes phrases), move-phrases (randomly moves phrases in the sentence), and resegment (changes the segmentation of the source ... See full document
11
Modifications of Machine Translation Evaluation Metrics by Using Word Embeddings
... and document level, which allows them to compute the similarity between two sequence of ...the adequacy on an ...use document-level embeddings as features and METEOR score as target to predict ... See full document
9
Exploring Adequacy Errors in Neural Machine Translation with the Help of Cross Language Aligned Word Embeddings
... word embeddings can be used to inform semantic analy- sis in NMT output ...human adequacy judgments and automatically generated semantic similarity scores is ...cross-language embeddings as the sole ... See full document
7
Personalized Machine Translation: Predicting Translational Preferences
... The difficulty to objectively determine whether one (automatic) translation is better than another has been repeatedly revealed in the MT literature. Our con- jecture is that one reason is individual preferences, ... See full document
7
Learning Bilingual Projections of Embeddings for Vocabulary Expansion in Machine Translation
... word embeddings to the task of vo- cabulary expansion in the context of statistical ma- chine translation ...word embeddings on both the languages and learn a model over a small seed lexicon to map ... See full document
7
Phrase based Unsupervised Machine Translation with Compositional Phrase Embeddings
... show that addition of two token vectors approxi- mately equivalent to the AND operation between their distributions over context words (we predict context / surrounding words with the Skip-gram model). This means that ... See full document
7
Machine Translation Evaluation for Arabic using Morphologically enriched Embeddings
... Furthermore, when paired to morpho-syntactic representations, the distributed lexical information seems to be a good alternative to n-gram metrics to obtain state-of-the-art results. In the future, we would like to use ... See full document
11
Shared Private Bilingual Word Embeddings for Neural Machine Translation
... Table 1 reports the results on the NIST Chinese- English test sets. It is observed that the Trans- former models significantly outperform SMT and RNNsearch models. Therefore, we decide to im- plement all of our ... See full document
10
From Extractive to Abstractive Summarization: A Journey
... large document- summary corpora have opened up new possibilities for using statistical text generation techniques for abstractive ...statistical machine translation as a generative text summarization ... See full document
7
Using Word Embeddings for Improving Statistical Machine Translation of Phrasal Verbs
... In this paper, we explore the usage of such rep- resentations for improving SMT of PVs. We pro- pose three strategies based on word embeddings. First, we employ continuous vectors of phrases learnt using neural ... See full document
5
Putting Evaluation in Context: Contextual Embeddings Improve Machine Translation Evaluation
... The Natural Language Inference (NLI) task is similar to MT evaluation (Pad´o et al., 2009): a good translation entails the reference and vice- versa. An irrelevant/wrong translation would be ... See full document
10
RUSE: Regressor Using Sentence Embeddings for Automatic Machine Translation Evaluation
... Ablation analysis. Tables 7 and 8 show that our metric with Quick-Thought feature only out- performed the state-of-the-art metrics in both segment- and system-level metrics tasks. Quick- Thought is an unsupervised model ... See full document
8
Attention Focusing for Neural Machine Translation by Bridging Source and Target Embeddings
... neural machine translation, a source se- quence of words is encoded into a vector from which a target sequence is generated in the decoding ...statistical machine translation, the associa- ... See full document
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