Tensor2Tensor for Neural Machine Translation
E899036
"Tensor2Tensor for Neural Machine Translation" is a research work introducing a modular, scalable library and methodology for training state-of-the-art neural machine translation models.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Tensor2Tensor for Neural Machine Translation canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T11003399 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tensor2Tensor for Neural Machine Translation Context triple: [Łukasz Kaiser, coAuthorOf, Tensor2Tensor for Neural Machine Translation]
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A.
Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
"Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation" is a seminal research paper that introduced the RNN encoder–decoder architecture to learn continuous phrase representations for improving statistical machine translation quality.
-
B.
Sequence to Sequence Learning with Neural Networks
"Sequence to Sequence Learning with Neural Networks" is a seminal 2014 paper that introduced the sequence-to-sequence (seq2seq) neural network framework for tasks like machine translation, laying the groundwork for many modern NLP models.
-
C.
Sequence transduction with recurrent neural networks
"Sequence transduction with recurrent neural networks" is a seminal research paper by Alex Graves that introduced powerful RNN-based methods for mapping input sequences to output sequences, influencing modern sequence-to-sequence and attention models in machine learning.
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D.
Attention Is All You Need
"Attention Is All You Need" is the landmark 2017 research paper that introduced the Transformer architecture and revolutionized modern natural language processing and sequence modeling.
-
E.
Neural Turing Machines (contributions)
Neural Turing Machines (contributions) refers to Oriol Vinyals’s work on augmenting neural networks with differentiable external memory to enable algorithmic reasoning and sequence learning beyond traditional architectures.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Tensor2Tensor for Neural Machine Translation Target entity description: "Tensor2Tensor for Neural Machine Translation" is a research work introducing a modular, scalable library and methodology for training state-of-the-art neural machine translation models.
-
A.
Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
"Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation" is a seminal research paper that introduced the RNN encoder–decoder architecture to learn continuous phrase representations for improving statistical machine translation quality.
-
B.
Sequence to Sequence Learning with Neural Networks
"Sequence to Sequence Learning with Neural Networks" is a seminal 2014 paper that introduced the sequence-to-sequence (seq2seq) neural network framework for tasks like machine translation, laying the groundwork for many modern NLP models.
-
C.
Sequence transduction with recurrent neural networks
"Sequence transduction with recurrent neural networks" is a seminal research paper by Alex Graves that introduced powerful RNN-based methods for mapping input sequences to output sequences, influencing modern sequence-to-sequence and attention models in machine learning.
-
D.
Attention Is All You Need
"Attention Is All You Need" is the landmark 2017 research paper that introduced the Transformer architecture and revolutionized modern natural language processing and sequence modeling.
-
E.
Neural Turing Machines (contributions)
Neural Turing Machines (contributions) refers to Oriol Vinyals’s work on augmenting neural networks with differentiable external memory to enable algorithmic reasoning and sequence learning beyond traditional architectures.
- F. None of above. chosen
Statements (37)
| Predicate | Object |
|---|---|
| instanceOf |
machine learning research
ⓘ
research work ⓘ scientific paper ⓘ |
| basedOn |
deep learning frameworks
ⓘ
neural networks ⓘ sequence-to-sequence models ⓘ |
| contributesTo | advancement of neural machine translation research ⓘ |
| emphasizes |
modularity
ⓘ
reusability of components ⓘ scalability ⓘ |
| evaluationDomain | machine translation benchmarks ⓘ |
| field |
deep learning
ⓘ
natural language processing ⓘ neural machine translation ⓘ |
| focusesOn |
modular library for neural machine translation
ⓘ
scalable training of neural machine translation models ⓘ standardized training methodology ⓘ state-of-the-art neural machine translation models ⓘ |
| goal |
enable reproducible machine translation experiments
ⓘ
provide scalable model training infrastructure ⓘ simplify training of neural machine translation models ⓘ |
| hasMethodology |
configuration-driven experiment management
ⓘ
standardized training and evaluation pipeline ⓘ |
| introduces | Tensor2Tensor library NERFINISHED ⓘ |
| language | English ⓘ |
| provides |
data input pipelines for translation tasks
ⓘ
modular components for model architectures ⓘ training scripts and configurations ⓘ |
| relatedTo |
Tensor2Tensor open-source project
NERFINISHED
ⓘ
Transformer-based translation models ⓘ sequence-to-sequence learning ⓘ |
| supports |
experimentation with different model hyperparameters
ⓘ
multiple neural machine translation architectures ⓘ |
| targetsUsers |
machine learning researchers
ⓘ
neural machine translation practitioners ⓘ |
| usedFor |
benchmarking neural machine translation systems
ⓘ
training translation models on large datasets ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: Tensor2Tensor for Neural Machine Translation Description of subject: "Tensor2Tensor for Neural Machine Translation" is a research work introducing a modular, scalable library and methodology for training state-of-the-art neural machine translation models.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.
subject surface form:
Łukasz Kaiser