[PDF] Top 20 Learning Joint Semantic Parsers from Disjoint Data
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Learning Joint Semantic Parsers from Disjoint Data
... Semantic parsing aims to automatically predict formal representations of meaning underlying nat- ural language, and has been useful in question an- swering (Shen and Lapata, 2007), text-to-scene generation (Coyne ... See full document
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Joint Concept Learning and Semantic Parsing from Natural Language Explanations
... for Learning from Natural Language (LNL) against baselines described above for n = 10 labeled ...training data, while ESA fails due to the lack of topical asso- ciations in ... See full document
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Joint Semantic Relevance Learning with Text Data and Graph Knowledge
... text learning and the graph knowledge learning: the pre-trained systems (JT-prt and GR- prt) significantly outperform the random initial- ized systems (JT-rand and ...This from another perspective ... See full document
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Weakly Supervised Learning of Semantic Parsers for Mapping Instructions to Actions
... task from MacMahon et ...the data, aligned traces to instructions, and merged traces created by different ...the learning and interpretation tasks, we also created a new dataset that includes only ... See full document
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Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision
... 2013), because 18.5% of the original dataset were found to be “not answerable”. It consists of 3,098 question-answer pairs for training and 1,639 for testing, which were collected using Google Sug- gest API, and the ... See full document
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Fast Domain Adaptation of Semantic Parsers via Paraphrase Attention
... We implemented our models using Torch 7. All baseline model hyper-parameters were tuned on validation data. To test the performance gain, our models use the same hyper-parameters as the base- line model. To ... See full document
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Improved Semantic Parsers For If Then Statements
... ing data to exceed the ...training data to do ...make learning harder (even with layer-wise pre- ...more data, NN could likely find representations that outperformed manual feature ... See full document
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Learning Semantic Parsers from Denotations with Latent Structured Alignments and Abstract Programs
... The first stage (i.e., mapping a question onto an abstract program) is handled with a sequence-to- sequence model. The second stage (i.e., program instantation) is approached with local classifiers: one per slot in the ... See full document
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Joint Learning of Preposition Senses and Semantic Roles of Prepositional Phrases
... learn semantic role assignment of different constituents for one task (SRL), while we attempt to jointly learn two tasks (WSD and SRL) for one ...full joint probability distribution of both ...a ... See full document
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Scaling Semantic Parsers with On the Fly Ontology Matching
... Freebase data, at which point perfor- mance on the development set had ...rameters from Section ...results from DCS (Liang et ...CCG learning approach that is most closely related to our ... See full document
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Cooperative Learning of Disjoint Syntax and Semantics
... There has been considerable attention devoted to models that learn to jointly infer an ex- pression’s syntactic structure and its seman- tics. Yet, Nangia and Bowman (2018) has re- cently shown that the current best ... See full document
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Using String Kernels for Learning Semantic Parsers
... Our system, K RISP (Kernel-based Robust In- terpretation for Semantic Parsing), takes NL sen- tences paired with their formal meaning represen- tations as training data. The productions of the for- mal MRL ... See full document
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Joint Unsupervised Learning of Semantic Representation of Words and Roles in Dependency Trees
... We use a combination of the Gigaword corpus and the Wikipedia 2013 dump as the training data (ap- proximately 2.5 billion words). The dependency trees are produced by Stanford neural network parser (Chen and ... See full document
7
Semantic Medical Image Analysis For Combining Visual Features
... the semantic substance of a picture and hard to give great outcomes as per the predefined classifications in the therapeutic area by utilizing less restorative ...of disjoint semantic tokens with ... See full document
7
Language to Code: Learning Semantic Parsers for If This Then That Recipes
... build semantic parsers that al- low users to describe recipes in natural language and have them automatically mapped to exe- cutable ...pairs from the ...resulting data is extremely noisy for ... See full document
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Large scale Semantic Parsing via Schema Matching and Lexicon Extension
... different semantic parsers for databases like GeoQuery and ATIS (including parsers produced by UBL) have achieved F1 scores of ...for learning seman- tic parsers to achieve strong ... See full document
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Semantic Lexicon Construction: Learning from Unlabeled Data via Spectral Analysis
... previous semantic lexicon studies, we evalu- ate on the classification of lemma-form ...training/test data, we extracted all the non-proper nouns which appeared at least twice as the head word of a noun ... See full document
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Joint Morphological and Syntactic Disambiguation
... toward joint inference of syntax and morphology, presenting joint models and testing approximation of these models with two parsers: one a pipeline (segmentation → tagging → parsing), the other ... See full document
10
Evaluating Induced CCG Parsers on Grounded Semantic Parsing
... our parsers against the tree- bank we found the unsupervised model only correctly predicted transitive verbs 20% of the time and ad- verbs 39% of the ...our data, we produced the correct transitive category ... See full document
6
Tweets Segmentation based on Popularity of Posted Tweets with Help of Hashtag
... state-of-the-art data, bringing about huge volumes of information created ...extremely from the loud and short nature of ...the semantic or setting data is all around safeguarded and ... See full document
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