Hossein Sameti

Also published as: H. Sameti


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SUTNLP at SemEval-2023 Task 4: LG-Transformer for Human Value Detection
Hamed Hematian Hemati | Sayed Hesam Alavian | Hossein Sameti | Hamid Beigy
Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)

When we interact with other humans, humanvalues guide us to consider the human element. As we shall see, value analysis in NLP hasbeen applied to personality profiling but not toargument mining. As part of SemEval-2023Shared Task 4, our system paper describes amulti-label classifier for identifying human val-ues. Human value detection requires multi-label classification since each argument maycontain multiple values. In this paper, we pro-pose an architecture called Label Graph Trans-former (LG-Transformer). LG-Transformeris a two-stage pipeline consisting of a trans-former jointly encoding argument and labelsand a graph module encoding and obtainingfurther interactions between labels. Using ad-versarial training, we can boost performanceeven further. Our best method scored 50.00 us-ing F1 score on the test set, which is 7.8 higherthan the best baseline method. Our code ispublicly available on Github.

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SUTNLP at SemEval-2023 Task 10: RLAT-Transformer for explainable online sexism detection
Hamed Hematian Hemati | Sayed Hesam Alavian | Hamid Beigy | Hossein Sameti
Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)

There is no simple definition of sexism, butit can be described as prejudice, stereotyping,or discrimination, especially against women,based on their gender. In online interactions,sexism is common. One out of ten Americanadults says that they have been harassed be-cause of their gender and have been the targetof sexism, so sexism is a growing issue. TheExplainable Detection of Online Sexism sharedtask in SemEval-2023 aims at building sexismdetection systems for the English language. Inorder to address the problem, we use largelanguage models such as RoBERTa and De-BERTa. In addition, we present Random LayerAdversarial Training (RLAT) for transformers,and show its significant impact on solving allsubtasks. Moreover, we use virtual adversar-ial training and contrastive learning to improveperformance on subtask A. Upon completionof subtask A, B, and C test sets, we obtainedmacro-F1 of 84.45, 67.78, and 52.52, respec-tively outperforming proposed baselines on allsubtasks. Our code is publicly available onGithub.

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Ebhaam at SemEval-2023 Task 1: A CLIP-Based Approach for Comparing Cross-modality and Unimodality in Visual Word Sense Disambiguation
Zeinab Taghavi | Parsa Haghighi Naeini | Mohammad Ali Sadraei Javaheri | Soroush Gooran | Ehsaneddin Asgari | Hamid Reza Rabiee | Hossein Sameti
Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)

This paper presents an approach to tackle the task of Visual Word Sense Disambiguation (Visual-WSD), which involves determining the most appropriate image to represent a given polysemous word in one of its particular senses. The proposed approach leverages the CLIP model, prompt engineering, and text-to-image models such as GLIDE and DALL-E 2 for both image retrieval and generation. To evaluate our approach, we participated in the SemEval 2023 shared task on “Visual Word Sense Disambiguation (Visual-WSD)” using a zero-shot learning setting, where we compared the accuracy of different combinations of tools, including “Simple prompt-based” methods and “Generated prompt-based” methods for prompt engineering using completion models, and text-to-image models for changing input modality from text to image. Moreover, we explored the benefits of cross-modality evaluation between text and candidate images using CLIP. Our experimental results demonstrate that the proposed approach reaches better results than cross-modality approaches, highlighting the potential of prompt engineering and text-to-image models to improve accuracy in Visual-WSD tasks. We assessed our approach in a zero-shot learning scenario and attained an accuracy of 68.75\% in our best attempt.


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Docalog: Multi-document Dialogue System using Transformer-based Span Retrieval
Sayed Hesam Alavian | Ali Satvaty | Sadra Sabouri | Ehsaneddin Asgari | Hossein Sameti
Proceedings of the Second DialDoc Workshop on Document-grounded Dialogue and Conversational Question Answering

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. This paper discusses our proposed approach, Docalog, for the DialDoc-22 (MultiDoc2Dial) shared task. Docalog identifies the most relevant knowledge in the associated document, in a multi-document setting. Docalog, is a three-stage pipeline consisting of (1) a document retriever model (DR. TEIT), (2) an answer span prediction model, and (3) an ultimate span picker deciding on the most likely answer span, out of all predicted spans. In the test phase of MultiDoc2Dial 2022, Docalog achieved f1-scores of 36.07% and 28.44% and SacreBLEU scores of 23.70% and 20.52%, respectively on the MDD-SEEN and MDD-UNSEEN folds.


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Ghmerti at SemEval-2019 Task 6: A Deep Word- and Character-based Approach to Offensive Language Identification
Ehsan Doostmohammadi | Hossein Sameti | Ali Saffar
Proceedings of the 13th International Workshop on Semantic Evaluation

This paper presents the models submitted by Ghmerti team for subtasks A and B of the OffensEval shared task at SemEval 2019. OffensEval addresses the problem of identifying and categorizing offensive language in social media in three subtasks; whether or not a content is offensive (subtask A), whether it is targeted (subtask B) towards an individual, a group, or other entities (subtask C). The proposed approach includes character-level Convolutional Neural Network, word-level Recurrent Neural Network, and some preprocessing. The performance achieved by the proposed model is 77.93% macro-averaged F1-score.


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SUT System Description for Anti-Spoofing 2017 Challenge
Mohammad Adiban | Hossein Sameti | Noushin Maghsoodi | Sajjad Shahsavari
Proceedings of the 29th Conference on Computational Linguistics and Speech Processing (ROCLING 2017)

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SUT Submission for NIST 2016 Speaker Recognition Evaluation: Description and Analysis
Hossein Zeinali | Hossein Sameti | Noushin Maghsoodi
Proceedings of the 29th Conference on Computational Linguistics and Speech Processing (ROCLING 2017)


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Active Learning in Noisy Conditions for Spoken Language Understanding
Hossein Hadian | Hossein Sameti
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers


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Building and Incorporating Language Models for Persian Continuous Speech Recognition Systems
M. Bahrani | H. Sameti | N. Hafezi | H. Movasagh
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)

In this paper building statistical language models for Persian language using a corpus and incorporating them in Persian continuous speech recognition (CSR) system are described. We used Persian Text Corpus for building the language models. First we preprocessed the texts of corpus by correcting the different orthography of words. Also, the number of POS tags was decreased by clustering POS tags manually. Then we extracted word based monogram and POS-based bigram and trigram language models from the corpus. We also present the procedure of incorporating language models in a Persian CSR system. By using the language models 27.4% reduction in word error rate was achieved in the best case.