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Learning a Neural Semantic Parser from User Feedback
... stages; in each stage, we recruited 10 new users (computer science graduate students) and asked them to issue at least 10 utterances each to the system and to provide feedback on the results. We considered results ... See full document
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Learning an Executable Neural Semantic Parser
... in neural networks and deep learning, there is a trend of reformulating semantic parsing as a machine translation ...because semantic parsing has been previously studied with statistical ... See full document
36
Joint Optimization of User desired Content in Multi document Summaries by Learning from User Feedback
... imize user-desired content selection while using a minimum amount of user feedback and ...active learning to further reduce the required ... See full document
11
Improving a Neural Semantic Parser by Counterfactual Learning from Human Bandit Feedback
... sample from a list of 30 cities from France, Germany and the ...it from the list of objects which are located in the prior sampled city and which have a name ...sampling from the four primary ... See full document
11
Can Neural Machine Translation be Improved with User Feedback?
... counterfactual learning with determin- istically logged feedback for statistical machine translation ...bandit feedback is then simulated from 40k News Commentary (NC) ...weak feedback, ... See full document
14
ANALYSING AND SIMULATING USER FEEDBACK IN NEURAL NETWORK
... of user feedback is discussed so as to enhance the quality of current E-learning ...for user feedback are described. We organized the end user feedback survey during our ... See full document
10
Learning from Chunk based Feedback in Neural Machine Translation
... investigate learning from partial feedback in neural machine transla- tion (NMT), when partial feedback is col- lected by asking users to highlight a cor- rect chunk of a ...such ... See full document
6
Transfer Learning for Neural Semantic Parsing
... Full semantic graphs can be expensive to an- notate, and efforts to date have been fragmented across different formalisms, leading to a limited amount of annotated data in any single ...Using neural ... See full document
9
Growing Semantic Grammars
... Three learning methods are employed in the acquisition of semantic mappings from unseen data: i parser predictions, ii hidden understanding model, and iii end-user paraphrases.. A baseli[r] ... See full document
6
Response based Learning for Machine Translation of Open domain Database Queries
... response-based learning for SMT. The problem lies in the incomplete nature of semantic parsing databases, where terms that are not parsed into logical forms in one context make a crucial difference in ... See full document
6
Retrieval and blur detection of images in mobile display devices using recent technologies
... obtained from the past user interactions with the system is ...different learning techniques, a useful distinction can be made between short-term learning within a single query session and ... See full document
10
Driving Semantic Parsing from the World’s Response
... to semantic parsing, the task of converting text to a formal meaning representation, rely on annotated training data mapping sentences to logi- cal ...novel learning algorithms capable of predicting complex ... See full document
10
Building a Semantic Parser Overnight
... From the seed lexicon, the domain-general grammar (Table 2) constructs noun phrases (NP), verbs phrases (VP), and complementizer phrase (CP), all of which denote unary logical forms. Broadly speaking, the rules ... See full document
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Learning Semantic Parsers from Denotations with Latent Structured Alignments and Abstract Programs
... Earlier work has used lexicon mappings (Zettle- moyer and Collins, 2007; Wong and Mooney, 2007; Lu et al., 2008; Kwiatkowski et al., 2010) to model correspondences between programs and natural language. However, these ... See full document
12
Reducing Grounded Learning Tasks To Grammatical Inference
... sense, learning a semantic parser seems to go beyond the well-studied task of unsupervised grammar ...only learning a grammar for the form-side of language, ...‘grounded’ learning tasks ... See full document
10
Polaris: Lymba’s Semantic Parser
... Lymba’s semantic parser. Polaris is a supervised semantic parser that given text extracts semantic ...relations from a wide variety of lexico-syntactic patterns, including ... See full document
7
TRANX: A Transition based Neural Abstract Syntax Parser for Semantic Parsing and Code Generation
... of semantic parsers, particularly neu- ral network-based ones has generally focused on a small subset of tasks — in order to ensure the syntactic well-formedness of generated MRs, a parser is usually ... See full document
6
Weakly Supervised Training of Semantic Parsers
... author from Austin, Texas,” was converted into the candidate query “author from Austin, ...relations from the knowledge base; candidate queries without satisfac- tory logical forms were ... See full document
12
On Interdisciplinary Comparative Study of Analogical Feedback/Assessment Models Applied in Blended Learning Versus Computer Aided Learning using Artificial Neural Networks
... all learning activities.” Blended learning is a blending of different learning methods, techniques and resources and applying them in an interactively meaningful learning ...blended ... See full document
13
Personalized Neural Embeddings for Collaborative Filtering with Text
... in the 3D space by projecting the high-dimensional word vectors using TensorFlow 1 as shown in Fig- ure 3. The top nearest neighbors of drug are: shot, shoots, gang, murder, killing, rape, stabbed, truck, school, police, ... See full document
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