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KorNLI and KorSTS: New Benchmark Datasets for Korean Natural Language Understanding
Natural language inference (NLI) and semantic textual similarity (STS) a...
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Exploring Software Naturalness throughNeural Language Models
The Software Naturalness hypothesis argues that programming languages ca...
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Use of Machine Translation to Obtain Labeled Datasets for Resource-Constrained Languages
The large annotated datasets in NLP are overwhelmingly in English. This ...
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AI4D – African Language Program
Advances in speech and language technologies enable tools such as voice-...
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Multilingual Argument Mining: Datasets and Analysis
The growing interest in argument mining and computational argumentation ...
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SICKNL: A Dataset for Dutch Natural Language Inference
We present SICK-NL (read: signal), a dataset targeting Natural Language ...
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A Normative approach to Attest Digital Discrimination
Digital discrimination is a form of discrimination whereby users are aut...
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DAVE: Deriving Automatically Verilog from English
While specifications for digital systems are provided in natural language, engineers undertake significant efforts to translate them into the programming languages understood by compilers for digital systems. Automating this process allows designers to work with the language in which they are most comfortable –the original natural language – and focus instead on other downstream design challenges. We explore the use of state-of-the-art machine learning (ML) to automatically derive Verilog snippets from English via fine-tuning GPT-2, a natural language ML system. We describe our approach for producing a suitable dataset of novice-level digital design tasks and provide a detailed exploration of GPT-2, finding encouraging translation performance across our task sets (94.8 tasks.
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