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Document 0: Source: https://julien-ser.github.io/JulienSerbanescu/ Type: Unknown Content Preview: Julien Serbanescu Julien Serbanescu... -------------------------------------------------------------------------------- Document 1: Source: docs/pdfs\paper.pdf Type: Unknown Content Preview: UnAnswGen: A Systematic Approach for Generating Unanswerable Questions in Machine Reading Comprehens... -------------------------------------------------------------------------------- Document 2: Source: docs/pdfs\paper.pdf Type: Unknown Content Preview: Unlike existing datasets like SQuAD2.0, which do not account for the reasons behind question unanswe... -------------------------------------------------------------------------------- Document 3: Source: docs/pdfs\paper.pdf Type: Unknown Content Preview: query reformulation. The resulting UnAnswGen dataset and asso- ciated software workflow are made pub... -------------------------------------------------------------------------------- Document 4: Source: docs/pdfs\paper.pdf Type: Unknown Content Preview: on the first page. Copyrights for components of this work owned by others than the author(s) must be... -------------------------------------------------------------------------------- Document 5: Source: docs/pdfs\paper.pdf Type: Unknown Content Preview: Development in Information Retrieval in the Asia Pacific Region (SIGIR-AP β24), December 9β12, 2024,... -------------------------------------------------------------------------------- Document 6: Source: docs/pdfs\paper.pdf Type: Unknown Content Preview: should avoid responding rather than making uncertain guesses, demonstrating their language comprehen... -------------------------------------------------------------------------------- Document 7: Source: docs/pdfs\paper.pdf Type: Unknown Content Preview: systems advance to meet the complexity of real-world information needs, there is an increasing deman... -------------------------------------------------------------------------------- Document 8: Source: docs/pdfs\paper.pdf Type: Unknown Content Preview: SIGIR-AP β24, December 9β12, 2024, Tokyo, Japan Hadiseh Moradisani, Fattane Zarrinkalam, Julien Serb... -------------------------------------------------------------------------------- Document 9: Source: docs/pdfs\paper.pdf Type: Unknown Content Preview: SQuAD2-CR [17] Wikip edia Cr owdsourcing (86,821 - 43,498) 6 [19] 2020 Dur eader [11] Chinese search... -------------------------------------------------------------------------------- Document 10: Source: docs/pdfs\paper.pdf Type: Unknown Content Preview: instance, it contains only 3,350 unanswerable questions labeled with No Information. Moreover, as th... -------------------------------------------------------------------------------- Document 11: Source: docs/pdfs\paper.pdf Type: Unknown Content Preview: ing unanswerable questions and enables the exploration of various causes of unanswerability. The onl... -------------------------------------------------------------------------------- Document 12: Source: docs/pdfs\paper.pdf Type: Unknown Content Preview: unanswerable questions into answerable ones. To develop a multi-label MRC dataset with unanswerable ... -------------------------------------------------------------------------------- Document 13: Source: docs/pdfs\paper.pdf Type: Unknown Content Preview: mation for each input question. Second, the generated candidate unanswerable questions are evaluated... -------------------------------------------------------------------------------- Document 14: Source: docs/pdfs\paper.pdf Type: Unknown Content Preview: The advantages of our work are twofold: (1) Our implementation of the proposed software workflow all... -------------------------------------------------------------------------------- Document 15: Source: docs/pdfs\paper.pdf Type: Unknown Content Preview: of SQuAD2.0 that includes multi-labeled unanswerable questions. Figure 1 presents the overview of ou... -------------------------------------------------------------------------------- Document 16: Source: docs/pdfs\paper.pdf Type: Unknown Content Preview: UnAnswGen: A Systematic Approach for Generating Unanswerable Questions in Machine Reading Comprehens... -------------------------------------------------------------------------------- Document 17: Source: docs/pdfs\paper.pdf Type: Unknown Content Preview: linguistic dimensions such as entity swap, number swap, negation, antonym, mutual exclusion, and no ... -------------------------------------------------------------------------------- Document 18: Source: docs/pdfs\paper.pdf Type: Unknown Content Preview: passage, and its candidate unanswerable questions. We have imple- mented and integrated a comprehens... -------------------------------------------------------------------------------- Document 19: Source: docs/pdfs\paper.pdf Type: Unknown Content Preview: placement: For each entity in the input answerable question, we replace it with another entity of th... -------------------------------------------------------------------------------- Document 20: Source: docs/pdfs\paper.pdf Type: Unknown Content Preview: corresponding context. Number Swap. Number Swap involves modifying a question to potentially render ... -------------------------------------------------------------------------------- Document 21: Source: docs/pdfs\paper.pdf Type: Unknown Content Preview: Time magazine named her one of the most 100 influential people of the century? could be Time magazin... -------------------------------------------------------------------------------- Document 22: Source: docs/pdfs\paper.pdf Type: Unknown Content Preview: (2) Replacement: Replace each identified word with its antonym, ensuring the modified question remai... -------------------------------------------------------------------------------- Document 23: Source: docs/pdfs\paper.pdf Type: Unknown Content Preview: SIGIR-AP β24, December 9β12, 2024, Tokyo, Japan Hadiseh Moradisani, Fattane Zarrinkalam, Julien Serb... -------------------------------------------------------------------------------- Document 24: Source: docs/pdfs\paper.pdf Type: Unknown Content Preview: of her music?, utilizing the Detection and Removal approach might lead to a question such as BeyoncΓ©... -------------------------------------------------------------------------------- Document 25: Source: docs/pdfs\paper.pdf Type: Unknown Content Preview: formation available in the given context, the question becomes inherently unanswerable. For instance... -------------------------------------------------------------------------------- Document 26: Source: docs/pdfs\paper.pdf Type: Unknown Content Preview: No Information. Similar to [33], to modify the original answer- able questions by considering this c... -------------------------------------------------------------------------------- Document 27: Source: docs/pdfs\paper.pdf Type: Unknown Content Preview: California is also home to a large homegrown surf and skateboard cul- ture.... This method ensures t... -------------------------------------------------------------------------------- Document 28: Source: docs/pdfs\paper.pdf Type: Unknown Content Preview: ππ , and for each candidate unanswerable question (ππ,ππ ) βπΆππ , we conduct the following evaluatio... -------------------------------------------------------------------------------- Document 29: Source: docs/pdfs\paper.pdf Type: Unknown Content Preview: ππ , denoted as πβ² π , to πππ , and attribute ππ as the reason for the unanswerability of πβ² π . The... -------------------------------------------------------------------------------- Document 30: Source: docs/pdfs\paper.pdf Type: Unknown Content Preview: are answerable. This dataset, developed through crowdsourcing, consists of a training set with 130,3... -------------------------------------------------------------------------------- Document 31: Source: docs/pdfs\paper.pdf Type: Unknown Content Preview: swerable candidate questions from a single modification process. Consequently, from the 86,821 answe... -------------------------------------------------------------------------------- Document 32: Source: docs/pdfs\paper.pdf Type: Unknown Content Preview: UnAnswGen: A Systematic Approach for Generating Unanswerable Questions in Machine Reading Comprehens... -------------------------------------------------------------------------------- Document 33: Source: docs/pdfs\paper.pdf Type: Unknown Content Preview: ele ctra-base-squad2 74.8 84.7 84.7 67.9 87.8 93.5 72.2 81.6 89.9 r oberta-large-squad 78.7 90 90 69... -------------------------------------------------------------------------------- Document 34: Source: docs/pdfs\paper.pdf Type: Unknown Content Preview: questions by returning a null or empty string when no appropri- ate answer is found within the conte... -------------------------------------------------------------------------------- Document 35: Source: docs/pdfs\paper.pdf Type: Unknown Content Preview: score indicating the modelβs certainty in its provided answer. For unanswerable questions, these mod... -------------------------------------------------------------------------------- Document 36: Source: docs/pdfs\paper.pdf Type: Unknown Content Preview: Table 4: Statistics on UnAnswGen dataset. Unansw erability Classes # of Questions Per centage A vera... -------------------------------------------------------------------------------- Document 37: Source: docs/pdfs\paper.pdf Type: Unknown Content Preview: of the final unanswerable question set. Specifically, questions from the Negation category account f... -------------------------------------------------------------------------------- Document 38: Source: docs/pdfs\paper.pdf Type: Unknown Content Preview: under the Negation label, 31.95 unanswerable questions under the Antonym label, and only 2.6 unanswe... -------------------------------------------------------------------------------- Document 39: Source: docs/pdfs\paper.pdf Type: Unknown Content Preview: indicates that the question is completely unrelated to the context, whereas a score of 1 indicates s... -------------------------------------------------------------------------------- Document 40: Source: docs/pdfs\paper.pdf Type: Unknown Content Preview: SIGIR-AP β24, December 9β12, 2024, Tokyo, Japan Hadiseh Moradisani, Fattane Zarrinkalam, Julien Serb... -------------------------------------------------------------------------------- Document 41: Source: docs/pdfs\paper.pdf Type: Unknown Content Preview: SQuAD2-CR+UnAnsw Gen 71.93 71.93 92.48 96.09 51.43 58.27 46.83 63.78 69.17 81.77 64.86 78.69 86.8 92... -------------------------------------------------------------------------------- Document 42: Source: docs/pdfs\paper.pdf Type: Unknown Content Preview: to evaluate the UnAnswerGen dataset against these criteria. Table 6 presents the results of Krippend... -------------------------------------------------------------------------------- Document 43: Source: docs/pdfs\paper.pdf Type: Unknown Content Preview: CR, which already includes multi-class labeling of unanswerable questions, and (2) the SQuAD2-CR tra... -------------------------------------------------------------------------------- Document 44: Source: docs/pdfs\paper.pdf Type: Unknown Content Preview: underwent training on both the enhanced SQuAD2.0+ UnAnswGen and the original SQuAD2-CR datasets, wit... -------------------------------------------------------------------------------- Document 45: Source: docs/pdfs\paper.pdf Type: Unknown Content Preview: BERTa, and 1% for Electra were observed. The balanced dataset successfully mitigates issues related ... -------------------------------------------------------------------------------- Document 46: Source: docs/pdfs\paper.pdf Type: Unknown Content Preview: hanced MRC datasets, with a focus on including multi-label unan- swerable questions. We have develop... -------------------------------------------------------------------------------- Document 47: Source: docs/pdfs\paper.pdf Type: Unknown Content Preview: flow to enrich other datasets, such as HotPotQA [35] and Natural Questions [16], with multi-label un... -------------------------------------------------------------------------------- Document 48: Source: docs/pdfs\paper.pdf Type: Unknown Content Preview: UnAnswGen: A Systematic Approach for Generating Unanswerable Questions in Machine Reading Comprehens... -------------------------------------------------------------------------------- Document 49: Source: docs/pdfs\paper.pdf Type: Unknown Content Preview: 3115β3119. [4] Christopher Clark and Matt Gardner. 2017. Simple and effective multi-paragraph readin... -------------------------------------------------------------------------------- Document 50: Source: docs/pdfs\paper.pdf Type: Unknown Content Preview: ciation for Computational Linguistics: EMNLP 2023. 7349β7360. [9] Kilem L Gwet. 2011. On the Krippen... -------------------------------------------------------------------------------- Document 51: Source: docs/pdfs\paper.pdf Type: Unknown Content Preview: questions. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 6529β6537. [13... -------------------------------------------------------------------------------- Document 52: Source: docs/pdfs\paper.pdf Type: Unknown Content Preview: [16] Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alb... -------------------------------------------------------------------------------- Document 53: Source: docs/pdfs\paper.pdf Type: Unknown Content Preview: 2022. Ptau: Prompt tuning for attributing unanswerable questions. In Proceedings of the 45th Interna... -------------------------------------------------------------------------------- Document 54: Source: docs/pdfs\paper.pdf Type: Unknown Content Preview: Answer Networks for Machine Reading Comprehension. In Proceedings of the 56th Annual Meeting of the ... -------------------------------------------------------------------------------- Document 55: Source: docs/pdfs\paper.pdf Type: Unknown Content Preview: Majumder, and Li Deng. 2016. Ms marco: A human-generated machine reading comprehension dataset. (201... -------------------------------------------------------------------------------- Document 56: Source: docs/pdfs\paper.pdf Type: Unknown Content Preview: Unanswerable questions for SQuAD. arXiv preprint arXiv:1806.03822 (2018). [30] Pranav Rajpurkar, Jia... -------------------------------------------------------------------------------- Document 57: Source: docs/pdfs\paper.pdf Type: Unknown Content Preview: 7th CCF International Conference, NLPCC 2018, Hohhot, China, August 26β30, 2018, Proceedings, Part I... -------------------------------------------------------------------------------- Document 58: Source: docs/pdfs\paper.pdf Type: Unknown Content Preview: [36] Changchang Zeng, Shaobo Li, Qin Li, Jie Hu, and Jianjun Hu. 2020. A survey on machine reading c... -------------------------------------------------------------------------------- Document 59: Source: docs/pdfs\resume.pdf Type: Unknown Content Preview: Julien Serbanescu 437-260-3435 | [email protected] | linkedin.com/in/julien-serbanescu-6ba52a241 ... -------------------------------------------------------------------------------- Document 60: Source: docs/pdfs\resume.pdf Type: Unknown Content Preview: Innovation/Creativity, Technical Communication, Mentoring Education Computer Engineering Co-op Major... -------------------------------------------------------------------------------- Document 61: Source: docs/pdfs\resume.pdf Type: Unknown Content Preview: on publications. /external-link-altUtilized various NLP methods such as NLTK and SpaCy in Python to ... -------------------------------------------------------------------------------- Document 62: Source: docs/pdfs\resume.pdf Type: Unknown Content Preview: application (Windows EXE) for cybersecurity threat detection and testing Organizations Guelph AI Clu... -------------------------------------------------------------------------------- Document 63: Source: docs/pdfs\resume.pdf Type: Unknown Content Preview: β’ /external-link-altLed a team to develop an AI assistant inspired by Jarvis from Iron Man, ensuring... -------------------------------------------------------------------------------- Document 64: Source: docs/pdfs\resume.pdf Type: Unknown Content Preview: β’ Developing robotics software using Docker and Linux, implementing Python-based control for Webots ... -------------------------------------------------------------------------------- Document 65: Source: docs/pdfs\resume.pdf Type: Unknown Content Preview: codes and provide medicine information, working on backend, delegating frontend and bridging β’ /exte... -------------------------------------------------------------------------------- Document 66: Source: docs/pdfs\resume.pdf Type: Unknown Content Preview: with a ReactJS frontend dashboard for configuring and managing academic research projects GAN to Gen... -------------------------------------------------------------------------------- Document 67: Source: BinThere.ai.m4a Type: audio_transcription Content Preview: All right, so today we're going to be quickly demoing binthere.ai. Now, what this does, it will det... -------------------------------------------------------------------------------- Document 68: Source: BinThere.ai.m4a Type: audio_transcription Content Preview: list it as biodegradable piece. So if I lower it down just because it's getting the white backgroun... -------------------------------------------------------------------------------- Document 69: Source: BinThere.ai.m4a Type: audio_transcription Content Preview: This is our new max score. And then as I listed there, and then it says it typically goes in a comp... -------------------------------------------------------------------------------- Document 70: Source: Synthia by Nuvela-AI.m4a Type: audio_transcription Content Preview: This is the user interface review of Cynthia, which is a service that makes research papers smarter... -------------------------------------------------------------------------------- Document 71: Source: Synthia by Nuvela-AI.m4a Type: audio_transcription Content Preview: load in certain fragments of other papers that have relevant pieces of information to what exactly... -------------------------------------------------------------------------------- Document 72: Source: Synthia by Nuvela-AI.m4a Type: audio_transcription Content Preview: machinery reading comprehension in order to find user sentiment. Something like that. And then we... -------------------------------------------------------------------------------- Document 73: Source: Synthia by Nuvela-AI.m4a Type: audio_transcription Content Preview: be another tool that the model context protocol system using and topic would actually be able to e... -------------------------------------------------------------------------------- Document 74: Source: Synthia by Nuvela-AI.m4a Type: audio_transcription Content Preview: to use. And it helps just make research smarter, more efficient, and better for users overall. No... -------------------------------------------------------------------------------- Document 75: Source: Synthia by Nuvela-AI.m4a Type: audio_transcription Content Preview: on our existing formatted proxy late-tech code. So that's my overview for our Cynthia front-end or... -------------------------------------------------------------------------------- |