Hossein Sameti

Also published as: H. Sameti


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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.