saeed entezari - Academia.edu (original) (raw)
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ENIM : National Engineering School of Monastir - TUNISIE
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Conversational argument retrieval is the problem of ranking argumentative texts in a collection o... more Conversational argument retrieval is the problem of ranking argumentative texts in a collection of focused arguments, ordered based on their quality. This study investigates eight different deep neural ranking models proposed in the literature with respect to their suitability to this task. In order to incorporate the insights from multiple models into an argument ranking, we further investigate a simple linear aggregation strategy. The Touché @ CLEF shared task on Conversational Argument Retrieval (Task 1) targets a retrieval scenario in a focused argument collection composed of roughly 387740 arguments to support argumentative conversation. We approach the problem of argument retrieval as finding relevant premises to the input query. Considering the lack of annotated data for the purpose of ranking, we take a distant supervision approach. Based on this approach, the conclusion (claim) of the arguments play the role of queries and the corresponding premises of the argument are the ...
Conversational argument retrieval is the problem of ranking argumentative texts in a collection o... more Conversational argument retrieval is the problem of ranking argumentative texts in a collection of focused arguments in order of their relevance to a textual query on different topics. In this notebook-paper for Touché by taking a distant supervision approach for constructing the query relevance information, we investigate seven different deep neural ranking models proposed in the literature with respect to their suitability to this task. In order to incorporate the insights from multiple models into an argument ranking, we further investigate a simple linear aggregation strategy. By retrieving relevant arguments using deep neural ranking models, it will be inspected to what extent the systems whose main concentration is on relevant documents, would be able to retrieve arguments which meet various quality dimensions of the arguments. Our test results suggest that the interaction-focused networks provide better performance compared to the representation-focused networks.
Conversational argument retrieval is the problem of ranking argumentative texts in a collection o... more Conversational argument retrieval is the problem of ranking argumentative texts in a collection of focused arguments, ordered based on their quality. This study investigates eight different deep neural ranking models proposed in the literature with respect to their suitability to this task. In order to incorporate the insights from multiple models into an argument ranking, we further investigate a simple linear aggregation strategy. The Touché @ CLEF shared task on Conversational Argument Retrieval (Task 1) targets a retrieval scenario in a focused argument collection composed of roughly 387740 arguments to support argumentative conversation. We approach the problem of argument retrieval as finding relevant premises to the input query. Considering the lack of annotated data for the purpose of ranking, we take a distant supervision approach. Based on this approach, the conclusion (claim) of the arguments play the role of queries and the corresponding premises of the argument are the ...
Conversational argument retrieval is the problem of ranking argumentative texts in a collection o... more Conversational argument retrieval is the problem of ranking argumentative texts in a collection of focused arguments in order of their relevance to a textual query on different topics. In this notebook-paper for Touché by taking a distant supervision approach for constructing the query relevance information, we investigate seven different deep neural ranking models proposed in the literature with respect to their suitability to this task. In order to incorporate the insights from multiple models into an argument ranking, we further investigate a simple linear aggregation strategy. By retrieving relevant arguments using deep neural ranking models, it will be inspected to what extent the systems whose main concentration is on relevant documents, would be able to retrieve arguments which meet various quality dimensions of the arguments. Our test results suggest that the interaction-focused networks provide better performance compared to the representation-focused networks.