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Response-based Learning for Grounded Machine Translation

Stefan Riezler, Patrick Simianer, Carolin Haas
2014 Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)  
We propose a novel learning approach for statistical machine translation (SMT) that allows to extract supervision signals for structured learning from an extrinsic response to a translation input.  ...  We show how to generate responses by grounding SMT in the task of executing a semantic parse of a translated query against a database.  ...  Introduction In this paper, we propose a novel approach for learning and evaluation in statistical machine translation (SMT) that borrows ideas from response-based learning for grounded semantic parsing  ... 
doi:10.3115/v1/p14-1083 dblp:conf/acl/RiezlerSH14 fatcat:t2fycy4okvbbddy5hqlmdwfppm

Response-based Learning for Machine Translation of Open-domain Database Queries

Carolin Haas, Stefan Riezler
2015 Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies  
Response-based learning allows to adapt a statistical machine translation (SMT) system to an extrinsic task by extracting supervision signals from task-specific feedback.  ...  In this paper, we elicit response signals for SMT adaptation by executing semantic parses of translated queries against the Freebase database.  ...  Acknowledgments This research was supported in part by DFG grant RI-2221/2-1 "Grounding Statistical Machine Translation in Perception and Action".  ... 
doi:10.3115/v1/n15-1149 dblp:conf/naacl/HaasR15 fatcat:t2oogdikm5be3gu7gvn5bhxrj4

Quantum Loop Topography for Machine Learning

Yi Zhang, Eun-Ah Kim
2017 Physical Review Letters  
The loop configuration is guided by characteristic response for defining the phase, which is Hall conductivity for the cases at hand.  ...  with machine learning will be broadly valuable.  ...  Three major strengths of our QLT-based machine learning approaches are 1) efficiency, 2) accuracy, and 3) versatility.  ... 
doi:10.1103/physrevlett.118.216401 pmid:28598670 fatcat:qzwui73ylbcu3ihwmblw5cbxim

A Survey on Retrieval-Augmented Text Generation [article]

Huayang Li and Yixuan Su and Deng Cai and Yan Wang and Lemao Liu
2022 arXiv   pre-print
It firstly highlights the generic paradigm of retrieval-augmented generation, and then it reviews notable approaches according to different tasks including dialogue response generation, machine translation  ...  For instance, x and y could be the dialogue history and the corresponding response for dialogue response generation, the text in the source language and the translation in the target language for machine  ...  In the rest of this section, we will review translation memory for both statistical machine translation (SMT) and neural machine translation (NMT).  ... 
arXiv:2202.01110v2 fatcat:vo6i5vq62raxtcry2xcbrm55be

TCT: A Cross-supervised Learning Method for Multimodal Sequence Representation [article]

Wubo Li, Wei Zou, Xiangang Li
2019 arXiv   pre-print
To tackle this, we propose the Transformer based Cross-modal Translator (TCT) to learn unimodal sequence representations by translating from other related multimodal sequences on a supervised learning  ...  The proposed method reports new state-of-the-art performance on video-grounded dialogue which indicates representations learned by TCT are more semantics compared to directly use unimodality.  ...  The research field of Multimodal Machine Learning brings some unique challenges for computational researchers given the heterogeneity of the data.  ... 
arXiv:1911.05186v1 fatcat:pwcnuz2qwfeghl2yts6yc7jpuu

Interoperability and machine-to-machine translation model with mappings to machine learning tasks [article]

Jacob Nilsson and Fredrik Sandin and Jerker Delsing
2019 arXiv   pre-print
We present alternative mathematical definitions of the translator learning task and mappings to similar machine learning tasks and solutions based on recent developments in machine learning.  ...  Here we define a translator-based operational interoperability model for interacting cyber-physical systems in mathematical terms, which includes system identification and ontology-based translation as  ...  Using the model, we propose learning strategies based on advances in natural language processing and graph neural networks, allowing for grounded translators.  ... 
arXiv:1903.10735v1 fatcat:v7e7nghfnja25chbrx5xdnfjme

Natural Language Processing Advancements By Deep Learning: A Survey [article]

Amirsina Torfi, Rouzbeh A. Shirvani, Yaser Keneshloo, Nader Tavaf, Edward A. Fox
2021 arXiv   pre-print
Natural Language Processing (NLP) helps empower intelligent machines by enhancing a better understanding of the human language for linguistic-based human-computer communication.  ...  This survey categorizes and addresses the different aspects and applications of NLP that have benefited from deep learning.  ...  Machine Translation Machine Translation (MT) is one of the areas of NLP that has been profoundly affected by the advances in deep learning.  ... 
arXiv:2003.01200v4 fatcat:riw6vvl24nfvboy56v2zfcidpu

Hyperspectral Data Processing: An Opportunity for End-To-End Processing

Marge Cole, Anne Wilson, Michael Little
2018 IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium  
Research has improved techniques in both onboard and ground-based processing to support other high data volume instruments.  ...  GROUND BASED PROCESSING Technologies such as cloud computing and machine learning are revolutionizing ground-based processing.  ...  Machine learning processes have also been applied to SAR data improving quality control and work flow.  ... 
doi:10.1109/igarss.2018.8517481 dblp:conf/igarss/ColeWL18 fatcat:ogjvqd3unbcidccy3hxaptbqga

Conversation Modeling with Neural Network

Jivan Y. Patil, Girish P. Potdar
2018 Asian Journal of Research in Computer Science  
Deep Neural Networks (DNNs) have achieved excellent performance for many of machine learning problems and are widely accepted for applications in the field of computer vision and supervised learning.  ...  This paper proposes a simple approach based on use of neural networks' recently proposed sequence to sequence framework.  ...  BLEU score used for machine translation.  ... 
doi:10.9734/ajrcos/2018/v1i324738 fatcat:f226jjtlebe7vl6rafbuzwkviy

Big Data Architecture for Environmental Analytics [chapter]

Ritaban Dutta, Cecil Li, Daniel Smith, Aruneema Das, Jagannath Aryal
2015 IFIP Advances in Information and Communication Technology  
The other two functional blocks, the Objective Identifier' and the 'Prior Extractor' were responsible to generate the ground truth based training target for the training phase and also for the testing  ...  for use with machine learning models.  ... 
doi:10.1007/978-3-319-15994-2_59 fatcat:qvxkw6w4hbgxvh6mjqj2heuaym

Developing Corpus-based Translation Methods between Informal and Formal Mathematics: Project Description [article]

Cezary Kaliszyk and Josef Urban and Jiri Vyskocil and Herman Geuvers
2014 arXiv   pre-print
The goal of this project is to (i) accumulate annotated informal/formal mathematical corpora suitable for training semi-automated translation between informal and formal mathematics by statistical machine-translation  ...  methods, (ii) to develop such methods oriented at the formalization task, and in particular (iii) to combine such methods with learning-assisted automated reasoning that will serve as a strong semantic  ...  Statistical machine learning (data-driven algorithm design) has been responsible for a number of recent AI breakthroughs, such as web search, query answering (IBM Watson), machine translation (Google Translate  ... 
arXiv:1405.3451v1 fatcat:iskrxhcukzh2fn7ihk7n4vasyu

On Machine Symbol Grounding and Optimization

Oliver Kramer
2011 International Journal of Cognitive Informatics and Natural Intelligence  
Symbol grounding is about whether these systems can, based on this data, construct symbols that serve as a vehicle for higher symbol-oriented cognitive processes.  ...  Machine learning and data mining techniques are geared towards finding structures and input-output relations in this data by providing appropriate interface algorithms that translate raw data into symbols  ...  This can also be observed in a lot of existing machine learning approaches for artificial agents.  ... 
doi:10.4018/ijcini.2011070105 fatcat:qvlfoyfkfvdl7bmtxykq4zwtza

Understanding Artificial Intelligence [article]

Aljoscha Burchardt, Julia Ebert
2018 Zenodo  
Aljoscha Burchardt sees a huge gap between the machines of today, which are able to act intelligently, and the quest for actually being intelligent in a truly human sense.  ...  We are trying to get our feet a little bit on the ground concerning machine learning but that is just the tip of the iceberg.  ...  Looking at the current state of AI and machine learning, machines are basically copying human behaviour, e.g. in the case of autonomous driving or smart translation systems.  ... 
doi:10.5281/zenodo.1211676 fatcat:rqwr3geqx5h5ri3k45ohvjipby

Machine learning based surrogate modeling with SVD enabled training for nonlinear civil structures subject to dynamic loading [article]

Siddharth S. Parida, Supratik Bose, Megan Butcher, Georgios Apostolakis, Prashant Shekhar
2022 arXiv   pre-print
In this paper, the authors propose a machine learning based surrogate model framework, which considers both these uncertainties in order to predict for unseen earthquakes.  ...  The framework is validated by using it to successfully predict the peak response of one-story and three-story buildings represented using stick models, subjected to unseen far-field ground motions.  ...  In this study, the authors have proposed a machine learning based surrogate model framework to accurately replicate finite element model response of a building for a given set of material parameters and  ... 
arXiv:2206.05720v1 fatcat:loedzqhdf5g2jokusdp3fixxxm

How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation [article]

Chia-Wei Liu, Ryan Lowe, Iulian V. Serban, Michael Noseworthy, Laurent Charlin, Joelle Pineau
2017 arXiv   pre-print
Recent works in response generation have adopted metrics from machine translation to compare a model's generated response to a single target response.  ...  We investigate evaluation metrics for dialogue response generation systems where supervised labels, such as task completion, are not available.  ...  Ritter et al. (2011b) formulate the unsupervised learning problem as one of translating a context into a candidate response.  ... 
arXiv:1603.08023v2 fatcat:eys6flfctbeudghhnmjwmens54
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