1 code implementation • dialdoc (ACL) 2022 • Sayed Hesam Alavian, Ali Satvaty, Sadra Sabouri, Ehsaneddin Asgari, Hossein Sameti
Information-seeking dialogue systems, including knowledge identification and response generation, aim to respond to users with fluent, coherent, and informative answers based on users’ needs.
no code implementations • COLING (TextGraphs) 2022 • Doratossadat Dastgheib, Ehsaneddin Asgari
Extraction of supportive premises for a mathematical problem can contribute to profound success in improving automatic reasoning systems.
no code implementations • 9 Apr 2024 • Omid Ghahroodi, Marzia Nouri, Mohammad Vali Sanian, Alireza Sahebi, Doratossadat Dastgheib, Ehsaneddin Asgari, Mahdieh Soleymani Baghshah, Mohammad Hossein Rohban
Evaluating Large Language Models (LLMs) is challenging due to their generative nature, necessitating precise evaluation methodologies.
no code implementations • 4 Feb 2024 • Mohammadreza Mofayezi, Reza Alipour, Mohammad Ali Kakavand, Ehsaneddin Asgari
Additionally, we propose the M3CelebA Dataset, a large-scale multi-modal and multilingual face dataset containing high-quality images, semantic segmentations, facial landmarks, and different captions for each image in multiple languages.
1 code implementation • 6 Dec 2023 • Hamed Hematian Hemati, Arash Lagzian, Moein Salimi Sartakhti, Hamid Beigy, Ehsaneddin Asgari
This paper introduces the detection of important news, in a previously unexplored area, and presents a new benchmarking dataset (Khabarchin) for detecting important news in the Persian language.
no code implementations • 15 May 2023 • Chunlan Ma, Ayyoob ImaniGooghari, Haotian Ye, Ehsaneddin Asgari, Hinrich Schütze
While natural language processing tools have been developed extensively for some of the world's languages, a significant portion of the world's over 7000 languages are still neglected.
1 code implementation • 31 Jan 2023 • Nailia Mirzakhmedova, Johannes Kiesel, Milad Alshomary, Maximilian Heinrich, Nicolas Handke, Xiaoni Cai, Barriere Valentin, Doratossadat Dastgheib, Omid Ghahroodi, Mohammad Ali Sadraei, Ehsaneddin Asgari, Lea Kawaletz, Henning Wachsmuth, Benno Stein
We present the Touch\'e23-ValueEval Dataset for Identifying Human Values behind Arguments.
2 code implementations • Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing 2022 • Sajad Mirzababaei, Amir Hossein Kargaran, Hinrich Schütze, Ehsaneddin Asgari
We create Hengam in the following concrete steps: (1) we develop HengamTagger, an extensible rule-based tool that can extract temporal expressions from a set of diverse language-specific patterns for any language of interest.
Ranked #1 on Temporal Tagging on HengamCorpus
1 code implementation • 3 Jun 2022 • Luisa März, Ehsaneddin Asgari, Fabienne Braune, Franziska Zimmermann, Benjamin Roth
To verify this assumption, we introduce a novel method, XPASC (eXPlainability-Association SCore), for measuring the generalization of a model trained with a weakly supervised dataset.
no code implementations • 14 Apr 2022 • Mohammad Reza Besharati, Mohammad Izadi, Ehsaneddin Asgari
Both of deductive and model checking verification techniques are relying on a notion of state and as a result, their underlying computational models are state dependent.
1 code implementation • EMNLP 2021 • Luisa März, Ehsaneddin Asgari, Fabienne Braune, Franziska Zimmermann, Benjamin Roth
The knowledge is captured in labeling functions, which detect certain regularities or patterns in the training samples and annotate corresponding labels for training.
no code implementations • 21 Dec 2020 • Ehsaneddin Asgari, Masoud Jalili Sabet, Philipp Dufter, Christopher Ringlstetter, Hinrich Schütze
This method's hypothesis is that the aggregation of different granularities of text for certain language pairs can help word-level alignment.
1 code implementation • SEMEVAL 2020 • Ehsaneddin Asgari, Christoph Ringlstetter, Hinrich Sch{\"u}tze
This paper describes EmbLexChange, a system introduced by the {``}Life-Language{''} team for SemEval-2020 Task 1, on unsupervised detection of lexical-semantic changes.
no code implementations • 16 May 2020 • Ehsaneddin Asgari, Christoph Ringlstetter, Hinrich Schütze
This paper describes EmbLexChange, a system introduced by the "Life-Language" team for SemEval-2020 Task 1, on unsupervised detection of lexical-semantic changes.
no code implementations • LREC 2020 • Ehsaneddin Asgari, Fabienne Braune, Benjamin Roth, Christoph Ringlstetter, Mohammad R. K. Mofrad
We introduce a method called DomDrift to mitigate the huge domain mismatch between Bible and Twitter by a confidence weighting scheme that uses domain-specific embeddings to compare the nearest neighbors for a candidate sentiment word in the source (Bible) and target (Twitter) domain.
no code implementations • EMNLP 2017 • Ehsaneddin Asgari, Hinrich Schütze
We present SuperPivot, an analysis method for low-resource languages that occur in a superparallel corpus, i. e., in a corpus that contains an order of magnitude more languages than parallel corpora currently in use.
no code implementations • 3 Oct 2016 • Hinrich Schuetze, Heike Adel, Ehsaneddin Asgari
We introduce the first generic text representation model that is completely nonsymbolic, i. e., it does not require the availability of a segmentation or tokenization method that attempts to identify words or other symbolic units in text.
no code implementations • WS 2016 • Ehsaneddin Asgari, Mohammad R. K. Mofrad
WELD is defined as divergence between unified similarity distribution of words between languages.
no code implementations • 1 Dec 2015 • Ehsaneddin Asgari, Kiavash Garakani, Mohammad R. K. Mofrad
We introduce a new approach for the efficient analysis of microbial communities.
1 code implementation • 17 Mar 2015 • Ehsaneddin Asgari, Mohammad R. K. Mofrad
Named bio-vectors (BioVec) to refer to biological sequences in general with protein-vectors (ProtVec) for proteins (amino-acid sequences) and gene-vectors (GeneVec) for gene sequences, this representation can be widely used in applications of deep learning in proteomics and genomics.