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Found 5 papers, 1 papers with code
Coping with Noisy Training Data Labels in Paraphrase Detection
We present new state-of-the-art benchmarks for paraphrase detection on all six languages in the Opusparcus sentential paraphrase corpus: English, Finnish, French, German, Russian, and Swedish.
Modeling Noise in Paraphrase Detection
Noisy labels in training data present a challenging issue in classification tasks, misleading a model towards incorrect decisions during training.
SemEval-2024 Shared Task 6: SHROOM, a Shared-task on Hallucinations and Related Observable Overgeneration Mistakes
This paper presents the results of the SHROOM, a shared task focused on detecting hallucinations: outputs from natural language generation (NLG) systems that are fluent, yet inaccurate.
Semantic Search as Extractive Paraphrase Span Detection
In this paper, we approach the problem of semantic search by framing the search task as paraphrase span detection, i. e. given a segment of text as a query phrase, the task is to identify its paraphrase in a given document, the same modelling setup as typically used in extractive question answering.
Grammatical Error Generation Based on Translated Fragments
We perform neural machine translation of sentence fragments in order to create large amounts of training data for English grammatical error correction.
