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Anas Himmi

dblp:347/8108 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2024
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Language models and text generation · 44% Image recognition and object detection · 44% Machine translation · 13%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computer vision › Image recognition and object detection › object detection
detector fusion		 0.812024
Enhanced Hallucination Detection in Neural Machine Translation through Simple Detector Aggregation · EMNLP 2024
Natural language and speech › Language models and text generation
hallucination detection		 0.812024
Enhanced Hallucination Detection in Neural Machine Translation through Simple Detector Aggregation · EMNLP 2024
Natural language and speech › Machine translation
neural machine translation		 0.212024
Enhanced Hallucination Detection in Neural Machine Translation through Simple Detector Aggregation · EMNLP 2024

Methods — techniques the papers use, named apart from their topics

detector aggregation · 0.8
YearPublicationVenuePosition
2024 Enhanced Hallucination Detection in Neural Machine Translation through Simple Detector Aggregation
abstract
Hallucinated translations pose significant threats and safety concerns when it comes to practical deployment of machine translation systems.Previous research works have identified that detectors exhibit complementary performance -different detectors excel at detecting different types of hallucinations.In this paper, we propose to address the limitations of individual detectors by combining them and introducing a straightforward method for aggregating multiple detectors.Our results demonstrate the efficacy of our aggregated detector, providing a promising step towards evermore reliable machine translation systems.
Anas Himmi, Guillaume Staerman, Marine Picot, Pierre Colombo, Nuno Miguel Guerreiro
 EMNLP1