[PDF] Top 20 Explaining Simple Natural Language Inference
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Explaining Simple Natural Language Inference
... The previous observations lead us to two impor- tant conclusions: for one, the justifications the an- notators provided were crucial to make us under- stand what was being annotated and what aspects of the guidelines ... See full document
12
Modeling Semantic Containment and Exclusion in Natural Language Inference
... because natural logic has a weaker proof theory than FOL, some inferences lie beyond its deductive ...of natural logic developed in this paper suc- ceeds in explaining a great variety of everyday ... See full document
8
Posing Fair Generalization Tasks for Natural Language Inference
... Artificially generated datasets have also been used extensively to gain analytic insights into what models are learning. These methods have the ad- vantage that the complexity of individual exam- ples can be precisely ... See full document
11
Compositional Lexical Semantics In Natural Language Inference
... Before sampling, we apply several filters in order to reduce the noise in the selected sen- tences. First, we use two POS taggers, the one distributed with Stanford CoreNLP (Man- ning et al. (2014)) and the one ... See full document
199
Enhanced LSTM for Natural Language Inference
... on natural language inference has been performed on rather small datasets with more con- ventional methods (refer to MacCartney (2009) for a good literature survey), which includes a large bulk of ... See full document
12
Annotation Artifacts in Natural Language Inference Data
... for natural language in- ference are created by presenting crowd work- ers with a sentence (premise), and asking them to generate three new sentences (hypotheses) that it entails, contradicts, or is ... See full document
6
Character level Intra Attention Network for Natural Language Inference
... Stanford Natural Language Inference (SNLI; Bowman et ...and simple, which limit the room for fine-grained comparisons between ...Sequential Inference Model (Chen et ... See full document
5
A Decomposable Attention Model for Natural Language Inference
... a simple neural architecture for nat- ural language ...ral Language Inference (SNLI) dataset, we ob- tain state-of-the-art results with almost an order of magnitude fewer parameters than ... See full document
7
A large annotated corpus for learning natural language inference
... in language can be reduced to questions of entailment and contradiction in ...both simple lexicalized models and neural network models perform well, and that the representations learned by a neural network ... See full document
11
Generating Token Level Explanations for Natural Language Inference
... a simple modification that runs the analysis twice: once for the premise sentence and once for the hypothesis sentence on the NLI model described in Section ... See full document
7
Unlearn Dataset Bias in Natural Language Inference by Fitting the Residual
... Statistical natural language inference (NLI) models are susceptible to learning dataset bias: superficial cues that happen to asso- ciate with the label on a particular dataset, but are not useful in ... See full document
11
Efficient Logical Inference for Semantic Processing
... quite natural to apply DCS trees, a simple and expressive semantic representation, to textual inference; however the use of abstract denotations to convey logical inference is somehow ... See full document
5
Natural Language and Inference in a Computer Game
... There is a wide range of different description log- ics today which add different extensions to a com- mon core. Of course, the more expressive these ex- tensions become, the more complex the reasoning problems are. ... See full document
7
InferLite: Simple Universal Sentence Representations from Natural Language Inference Data
... In this work we propose a lightweight version of InferSent, called InferLite. InferLite deviates from InferSent in that it does not use any recurrent con- nections and can generalize to multiple pre-trained word ... See full document
7
Natural Language Inference with Monotonicity
... an inference: all ↑ cat ↓ sleep ↑ . This replacement is extremely simple and ...more natural semantics of all, the semantics that carries an existential ... See full document
8
Dialogue Natural Language Inference
... Then, we demonstrate that NLI can be used to improve the consistency of dialogue models using a simple method where utterances are re-ranked using a NLI model trained on Dialogue NLI. The method results in fewer ... See full document
11
A Wide Coverage Symbolic Natural Language Inference System
... GF In GF, abstract syntax is comprised of: a) a number of syntactic categories, and b) a number of syntactic construction functions. The latter pro- vide the means to compose basic syntactic cate- gories into more ... See full document
6
A Linear Programming Formulation for Global Inference in Natural Language Tasks
... an inference algorithm that can produce a co- herent labeling of entities and relations in a given sen- ...satisfies natural constraints that exist on whether spe- cific entities can be the argument of ... See full document
8
Gaussian Transformer: A Lightweight Approach for Natural Language Inference
... Natural Language Inference (NLI), also known as Recog- nizing Textual Entailment (RTE), is a fundamental prob- lem in the research field of natural language understand- ing, which could ... See full document
8
CPSC Artificial Intelligence! Fall 2017" Jörg Denzinger" ICT 752"
... an application area, machine learning to add to represented knowledge (often using natural. language understanding) and restricted inference[r] ... See full document
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