Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Ye Zheng"'
Adversarial attacks have gained traction in order to identify potential vulnerabilities in neural ranking models (NRMs), but current attack methods often introduce grammatical errors, nonsensical expressions, or incoherent text fragments, which can b
Externí odkaz:
http://arxiv.org/abs/2305.01860
Knowledge graph completion (KGC) aims to infer missing knowledge triples based on known facts in a knowledge graph. Current KGC research mostly follows an entity ranking protocol, wherein the effectiveness is measured by the predicted rank of a maske
Externí odkaz:
http://arxiv.org/abs/2205.04105
Automatic dialogue coherence evaluation has attracted increasing attention and is crucial for developing promising dialogue systems. However, existing metrics have two major limitations: (a) they are mostly trained in a simplified two-level setting (
Externí odkaz:
http://arxiv.org/abs/2106.00507
BERT-based text ranking models have dramatically advanced the state-of-the-art in ad-hoc retrieval, wherein most models tend to consider individual query-document pairs independently. In the mean time, the importance and usefulness to consider the cr
Externí odkaz:
http://arxiv.org/abs/2104.08523
Automatically evaluating dialogue coherence is a challenging but high-demand ability for developing high-quality open-domain dialogue systems. However, current evaluation metrics consider only surface features or utterance-level semantics, without ex
Externí odkaz:
http://arxiv.org/abs/2010.03994
Target-guided open-domain conversation aims to proactively and naturally guide a dialogue agent or human to achieve specific goals, topics or keywords during open-ended conversations. Existing methods mainly rely on single-turn datadriven learning an
Externí odkaz:
http://arxiv.org/abs/2002.01196
We are investigating a prototype virtual pinhole positron emission tomography (VP-PET) system for small animal imaging applications. The PET detector modules were made up of 1.3 mm lutetium-yttrium oxyorthosilicate (LYSO) arrays, and the insert detec
Externí odkaz:
http://arxiv.org/abs/1801.03225
We present the EpiReader, a novel model for machine comprehension of text. Machine comprehension of unstructured, real-world text is a major research goal for natural language processing. Current tests of machine comprehension pose questions whose an
Externí odkaz:
http://arxiv.org/abs/1606.02270
Understanding unstructured text is a major goal within natural language processing. Comprehension tests pose questions based on short text passages to evaluate such understanding. In this work, we investigate machine comprehension on the challenging
Externí odkaz:
http://arxiv.org/abs/1603.08884