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ShareScore release 0.9.0
Dataset results
4 results for “natural language generation”
NLAS-multi: A Multilingual Corpus of Automatically Generated Natural Language Argumentation Schemes
<p>The multilingual corpus of natural language argumentation schemes (NLAS-multi) consists of 3,810 natural language argumentation schemes of which 1,893 are in English and 1,917 in Spanish. It has a total of 253,516 words distributed in 118,493 words in English and 135,023 words in Spanish. In terms of inferences, our corpus has a total of 7,964 (3,949 in English and 4,015 in Spanish). Furthermore, the NLAS-multi corpus contains a total of 23,781 conflict relations between arguments in the same topic.</p>
Supplementary Material, Towards the LLM-Based Generation of Formal Specifications from Natural-Language Contracts: Early Experiments with Symboleo
<p>This repository contains all the files used in the experiments described in the paper "Towards the LLM-Based Generation of Formal Specifications from Natural-Language Contracts: Early Experiments with Symboleo", which appeared in "RAISE 2025: Requirements engineering for AI-powered SoftwarE", an ICSE 2025 workshop, Ottawa, May 3, 2025.</p>
Supplementary materials of the paper entitled: "Generating Natural Language Requirements via Adversarial Examples in Deep Learning"
<p>Supplementary materials of the paper entitled: "Generating Natural Language Requirements via Adversarial Examples in Deep Learning"</p> <p>File A: Datasets (.txt)</p> <p> A1: Webex</p> <p> A2: Zoom</p> <p> A3: Teams</p> <p> A4: Word</p> <p> A5: PowerPoint</p> <p> A6: Excel</p> <p> </p> <p>File B: Python Code (Both ours and baseline)</p> <p> B1: adversarial_samples.ipynb</p> <p> B2: Baseline.ipynb</p> <p> </p> <p>FIle C: Result Tables (.xlsx)</p> <p> C1: Table of perturbed outputs in Webex </p> <p> C2: Table of perturbed outputs in Zoom</p> <p> C3: Table of perturbed outputs in Teams</p> <p> C4: Table of perturbed outputs in Word</p> <p> C5: Table of perturbed outputs in PowerPoint</p> <p> C6: Table of perturbed outputs in Excel (Excel)</p> <p> </p> <p>File D: Trend of Adversarial Shifts (Graphs)</p> <p> D1: Adversarial shifts of office suit (LSTM)</p> <p> D2: Adversarial shifts of video conferencing suit (LSTM)</p> <p> D3: Non-Adversarial shifts of office suit (LSTM)</p> <p> D4: Non-Adversarial shifts of video conferencing suit(LSTM)</p> <p> D5: Adversarial shifts of office suit (GRU)</p> <p> D6: Adversarial shifts of video conferencing suit (GRU)</p> <p> D7: Non-Adversarial shifts of office suit (GRU)</p> <p> D8: Non-Adversarial shifts of video conferencing suit(GRU)</p> <p> D9: Adversarial shifts of office suit (Bi-LSTM)</p> <p> D10: Adversarial shifts of video conferencing suit (Bi-LSTM)</p> <p> D11: Non-Adversarial shifts of office suit (Bi-LSTM)</p> <p> D12: Non-Adversarial shifts of video conferencing suit(Bi-LSTM)</p> <p> </p> <p>File E: Adversarial Examples (.pdf)</p> <p> E1: Adversarial vs original in Webex</p> <p> E2: Adversarial vs original in Zoom</p> <p> E3: Adversarial vs original in Teams</p> <p> E4: Adversarial vs original in Word</p> <p> E5: Adversarial vs original in PowerPoint</p> <p> E6: Adversarial vs original in Excel</p> <p> </p> <p>File F: Questionnaire</p> <p> </p>
Supplementary materials of the paper entitled: "Generating Natural Language Requirements via Adversarial Examples in Deep Learning"
<p>Supplementary materials of the paper entitled:</p> <p>"Generating Natural Language Requirements via Adversarial Examples in Deep Learning"</p> <p>File A: Datasets (.txt)</p> <p> A1: Webex</p> <p> A2: Zoom</p> <p> A3: Teams</p> <p> A4: Word</p> <p> A5: Power Point</p> <p> A6: Excel</p> <p> </p> <p>File B: Python Code (Both Ours and Baseline)</p> <p> B1: adversarial_samples.ipynb</p> <p> B2: Baseline.ipynb</p> <p> </p> <p>FIle C: Result Tables (.xlsx)</p> <p> C1: Table of Perturbed outputs in WebEx </p> <p> C2: Table of Perturbed outputs in Zoom</p> <p> C3: Table of Perturbed outputs in Teams</p> <p> C4: Table of Perturbed outputs in Word</p> <p> C5: Table of Perturbed outputs in Power Point</p> <p> C6: Table of Perturbed outputs in Excel (Excel)</p> <p> </p> <p>File D: Trend of Adversarial Shifts (Graphs)</p> <p> D1: Adversarial shifts of office suit (LSTM)</p> <p> D2: Adversarial shifts of video conferencing suit (LSTM)</p> <p> D3: Non-Adversarial shifts of office suit (LSTM)</p> <p> D4: Non-Adversarial shifts of video conferencing suit(LSTM)</p> <p> D5: Adversarial shifts of office suit (GRU)</p> <p> D6: Adversarial shifts of video conferencing suit (GRU)</p> <p> D7: Non-Adversarial shifts of office suit (GRU)</p> <p> D8: Non-Adversarial shifts of video conferencing suit(GRU)</p> <p> D9: Adversarial shifts of office suit (Bi-LSTM)</p> <p> D10: Adversarial shifts of video conferencing suit (Bi-LSTM)</p> <p> D11: Non-Adversarial shifts of office suit (Bi-LSTM)</p> <p> D12: Non-Adversarial shifts of video conferencing suit(Bi-LSTM)</p> <p> </p> <p>File E: Adversarial Examples (.pdf)</p> <p> E1: Adversarial vs Original in WebEx</p> <p> E2: Adversarial vs Original in Zoom</p> <p> E3: Adversarial vs Original in Teams</p> <p> E4: Adversarial vs Original in Word</p> <p> E5: Adversarial vs Original in Power Point</p> <p> E6: Adversarial vs Original in Excel</p> <p> </p> <p>File F: Questionnaire</p> <p><br> <br> <br> <br> <br> <br> </p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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