On the Similarities Between Native, Non-native and Translated Texts

We present a computational analysis of three language varieties: native, advanced non-native, and translation. Our goal is to investigate the similarities and differences between non-native language productions and translations, contrasting both with native language. Using a collection of computational methods we establish three main results: (1) the three types of texts are easily distinguishable; (2) non-native language and translations are closer to each other than each of them is to native language; and (3) some of these characteristics depend on the source or native language, while others do not, reflecting, perhaps, unified principles that similarly affect translations and non-native language.Comment: ACL2016, 12 page

Reliable and Interpretable Drift Detection in Streams of Short Texts

Data drift is the change in model input data that is one of the key factors leading to machine learning models performance degradation over time. Monitoring drift helps detecting these issues and preventing their harmful consequences. Meaningful drift interpretation is a fundamental step towards effective re-training of the model. In this study we propose an end-to-end framework for reliable model-agnostic change-point detection and interpretation in large task-oriented dialog systems, proven effective in multiple customer deployments. We evaluate our approach and demonstrate its benefits with a novel variant of intent classification training dataset, simulating customer requests to a dialog system. We make the data publicly available.Comment: ACL2023 industry track (9 pages

Galectin-1 is essential for efficient liver regeneration following hepatectomy

Galectin-1 (Gal1) is a known immune/inflammatory regulator which actsboth extracellularly and intracellularly, modulating innate and adaptive immuneresponses. Here, we explored the role of Gal1 in liver regeneration using 70% partial hepatectomy (PHx) of C57BL/6 wild type and Gal1-knockout (Gal1-KO, Lgals1-/-) mice. Gene or protein expression, in liver samples collected at time intervals from 2 to 168 hours post-operation, was tested by either RT-PCR or by immunoblotting and immunohistochemistry, respectively. We demonstrated that Gal1 transcript and protein expression was induced in the liver tissue of wild type mice upon PHx. Liver regeneration following PHx was significantly delayed in the Gal1-KO compared to the control liver. This delay was accompanied by a decreased Akt phosphorylation, and accumulation of the hepatocyte nuclear p21 protein in the Gal1-KO versus control livers at 24 and 48 hours following PHx. Transcripts of several known regulators of inflammation, cell cycle and cell signaling, including some known PHx-induced genes, were aberrantly expressed (mainly down-regulated) in Gal1-KO compared to control livers at 2, 6 and 24 hours post-PHx. Transient steatosis, which is imperative for liver regeneration following PHx, was significantly delayed and decreased in the Gal1- KO compared to the control liver and was accompanied by a significantly decreased expression in the mutant liver of several genes encoding lipid metabolism regulators.Our results demonstrate that Gal1 protein is essential for efficient liver regeneration following PHx through the regulation of liver inflammation, hepatic cell proliferation, and the control of lipid storage in the regenerating liver.Fil: Potikha, Tamara. Hadassah Hebrew University Medical Center; IsraelFil: Ella, Ezra. Hadassah Hebrew University Medical Center; IsraelFil: Cerliani, Juan Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Biología y Medicina Experimental. Fundación de Instituto de Biología y Medicina Experimental. Instituto de Biología y Medicina Experimental; ArgentinaFil: Mizrahi, Lina. Hadassah Hebrew University Medical Center; IsraelFil: Pappo, Orit. Hadassah Hebrew University Medical Center; IsraelFil: Rabinovich, Gabriel Adrián. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Biología y Medicina Experimental. Fundación de Instituto de Biología y Medicina Experimental. Instituto de Biología y Medicina Experimental; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; ArgentinaFil: Galun, Eithan. Hadassah Hebrew University Medical Center; IsraelFil: Goldenberg, Daniel S.. Hadassah Hebrew University Medical Center; Israe

Predicting Question-Answering Performance of Large Language Models through Semantic Consistency

Semantic consistency of a language model is broadly defined as the model's ability to produce semantically-equivalent outputs, given semantically-equivalent inputs. We address the task of assessing question-answering (QA) semantic consistency of contemporary large language models (LLMs) by manually creating a benchmark dataset with high-quality paraphrases for factual questions, and release the dataset to the community. We further combine the semantic consistency metric with additional measurements suggested in prior work as correlating with LLM QA accuracy, for building and evaluating a framework for factual QA reference-less performance prediction -- predicting the likelihood of a language model to accurately answer a question. Evaluating the framework on five contemporary LLMs, we demonstrate encouraging, significantly outperforming baselines, results.Comment: EMNLP2023 GEM workshop, 17 page