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SemMT: A Semantic-Based Testing Approach for Machine Translation Systems
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Quantification in Natural Languages
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Generalized quantifiers and natural language
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Resolution of quantifier scope ambiguities
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Introduction to automata theory, languages, and computation
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Introduction to automata theory, languages and computation
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Similarity in languages and programs
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An Intensional Parametric Semantics For Vague Quantifiers
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Quantifiers in Language and Logic
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Response Quality in Survey Research with Children and Adolescents: The Effect of Labeled Response Options and Vague Quantifiers
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Testing Regex Generalizability And Its Implications: A Large-Scale Many-Language Measurement Study
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Metamorphic Testing for Machine Translations: MT4MT
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Detecting Failures of Neural Machine Translation in the Absence of Reference Translations
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Applied Machine Learning at Facebook: A Datacenter Infrastructure Perspective
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Rex: Symbolic Regular Expression Explorer
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Metamorphic Testing for Software Quality Assessment: A Study of Search Engines
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Effects of machine translation on collaborative work
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Inference of concise regular expressions and DTDs
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Learning Deterministic Regular Expressions for the Inference of Schemas from XML Data
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Exploring regular expression usage and context in Python
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A monte carlo method for metamorphic testing of machine translation services
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Machine translation testing via pathological invariance
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Multi-modal synthesis of regular expressions
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Quantification as a Major Module of Natural Language Semantics
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Search Engine Guided Neural Machine Translation
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OpusFilter: A Configurable Parallel Corpus Filtering Toolbox
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Tangled up in BLEU: Reevaluating the Evaluation of Automatic Machine Translation Evaluation Metrics
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Logical Inferences with Comparatives and Generalized Quantifiers
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The Helsinki submission to the AmericasNLP shared task
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Neural Generation of Regular Expressions from Natural Language with Minimal Domain Knowledge
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SemRegex: A Semantics-Based Approach for Generating Regular Expressions from Natural Language Specifications
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Has Machine Translation Achieved Human Parity? A Case for Document-level Evaluation
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Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
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SoftRegex: Generating Regex from Natural Language Descriptions using Softened Regex Equivalence
inferredcites →https://doi.org/10.18653/v1/n18-1120openalex-snapshot
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
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Enhanced LSTM for Natural Language Inference
inferredcites →https://doi.org/10.18653/v1/p18-1029openalex-snapshot
Zero-shot Learning of Classifiers from Natural Language Quantification
inferredcites →https://doi.org/10.18653/v1/p18-2019openalex-snapshot
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inferredcites →https://doi.org/10.18653/v1/p19-1116openalex-snapshot
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inferredcites →https://doi.org/10.18653/v1/p19-1269openalex-snapshot
Putting Evaluation in Context: Contextual Embeddings Improve Machine Translation Evaluation
inferredcites →https://doi.org/10.18653/v1/s17-2001openalex-snapshot
SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation
inferredcites →https://doi.org/10.18653/v1/w15-3049openalex-snapshot
chrF: character n-gram F-score for automatic MT evaluation
inferredcites →https://doi.org/10.18653/v1/w19-5358openalex-snapshot
YiSi - a Unified Semantic MT Quality Evaluation and Estimation Metric for Languages with Different Levels of Available Resources
inferredcites →https://doi.org/10.18653/v1/w19-5401openalex-snapshot
Findings of the WMT 2019 Shared Tasks on Quality Estimation
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Noisy Parallel Corpus Filtering through Projected Word Embeddings
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Proceedings of the 39th Annual Meeting on Association for Computational Linguistics - ACL '01
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A multilingual natural-language interface to regular expressions
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Manual and automatic evaluation of machine translation between European languages
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Proceedings of the 17th international conference on Computational linguistics -
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AI-Assisted Pipeline for Dynamic Generation of Trustworthy Health Supplement Content at Scale
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Wholes and Parts: The Limits of Composition
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Explaining and Harnessing Adversarial Examples
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Generating Natural Adversarial Examples
inferredcites →https://doi.org/10.48550/arxiv.1711.02173openalex-snapshot
Synthetic and Natural Noise Both Break Neural Machine Translation
inferredcites →https://doi.org/10.48550/arxiv.1806.09030openalex-snapshot
On Adversarial Examples for Character-Level Neural Machine Translation
inferredcites →https://doi.org/10.48550/arxiv.2002.12543openalex-snapshot
Metamorphic Testing: A New Approach for Generating Next Test Cases
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Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Design of Machine Automatic Translation System Based on Artificial Intelligence
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MetBench: A Numerical Expression Metamorphic Relations Benchmark Data Set
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Design of Automated English Intelligent Translation System Under Machine Learning
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Automated Translation Model Based on Deep Learning Algorithm
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Optimizing Machine Translation Algorithms through Empirical Study of Multi-modal Information Fusion
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Semantic Structure Invariance-Based Metamorphic Testing for Machine Translation Systems
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