Zobrazeno 1 - 10
of 23
pro vyhledávání: '"Dankin, Lena"'
Autor:
Ein-Dor, Liat, Toledo-Ronen, Orith, Spector, Artem, Gretz, Shai, Dankin, Lena, Halfon, Alon, Katz, Yoav, Slonim, Noam
Prompts are how humans communicate with LLMs. Informative prompts are essential for guiding LLMs to produce the desired output. However, prompt engineering is often tedious and time-consuming, requiring significant expertise, limiting its widespread
Externí odkaz:
http://arxiv.org/abs/2408.04560
Autor:
Shnarch, Eyal, Halfon, Alon, Gera, Ariel, Danilevsky, Marina, Katsis, Yannis, Choshen, Leshem, Cooper, Martin Santillan, Epelboim, Dina, Zhang, Zheng, Wang, Dakuo, Yip, Lucy, Ein-Dor, Liat, Dankin, Lena, Shnayderman, Ilya, Aharonov, Ranit, Li, Yunyao, Liberman, Naftali, Slesarev, Philip Levin, Newton, Gwilym, Ofek-Koifman, Shila, Slonim, Noam, Katz, Yoav
Text classification can be useful in many real-world scenarios, saving a lot of time for end users. However, building a custom classifier typically requires coding skills and ML knowledge, which poses a significant barrier for many potential users. T
Externí odkaz:
http://arxiv.org/abs/2208.01483
Autor:
Shnarch, Eyal, Gera, Ariel, Halfon, Alon, Dankin, Lena, Choshen, Leshem, Aharonov, Ranit, Slonim, Noam
In real-world scenarios, a text classification task often begins with a cold start, when labeled data is scarce. In such cases, the common practice of fine-tuning pre-trained models, such as BERT, for a target classification task, is prone to produce
Externí odkaz:
http://arxiv.org/abs/2203.10581
Autor:
Ein-Dor, Liat, Shnayderman, Ilya, Spector, Artem, Dankin, Lena, Aharonov, Ranit, Slonim, Noam
In recent years, pretrained language models have revolutionized the NLP world, while achieving state of the art performance in various downstream tasks. However, in many cases, these models do not perform well when labeled data is scarce and the mode
Externí odkaz:
http://arxiv.org/abs/2201.02026
We describe the 2021 Key Point Analysis (KPA-2021) shared task on key point analysis that we organized as a part of the 8th Workshop on Argument Mining (ArgMining 2021) at EMNLP 2021. We outline various approaches and discuss the results of the share
Externí odkaz:
http://arxiv.org/abs/2110.10577
We suggest a model for metaphor interpretation using word embeddings trained over a relatively large corpus. Our system handles nominal metaphors, like "time is money". It generates a ranked list of potential interpretations of given metaphors. Candi
Externí odkaz:
http://arxiv.org/abs/2010.02665
Autor:
Ein-Dor, Liat, Gera, Ariel, Toledo-Ronen, Orith, Halfon, Alon, Sznajder, Benjamin, Dankin, Lena, Bilu, Yonatan, Katz, Yoav, Slonim, Noam
Extraction of financial and economic events from text has previously been done mostly using rule-based methods, with more recent works employing machine learning techniques. This work is in line with this latter approach, leveraging relevant Wikipedi
Externí odkaz:
http://arxiv.org/abs/1911.10783
Autor:
Ein-Dor, Liat, Shnarch, Eyal, Dankin, Lena, Halfon, Alon, Sznajder, Benjamin, Gera, Ariel, Alzate, Carlos, Gleize, Martin, Choshen, Leshem, Hou, Yufang, Bilu, Yonatan, Aharonov, Ranit, Slonim, Noam
Publikováno v:
AAAI 2020
One of the main tasks in argument mining is the retrieval of argumentative content pertaining to a given topic. Most previous work addressed this task by retrieving a relatively small number of relevant documents as the initial source for such conten
Externí odkaz:
http://arxiv.org/abs/1911.10763
Autor:
Orbach, Matan, Bilu, Yonatan, Gera, Ariel, Kantor, Yoav, Dankin, Lena, Lavee, Tamar, Kotlerman, Lili, Mirkin, Shachar, Jacovi, Michal, Aharonov, Ranit, Slonim, Noam
In Natural Language Understanding, the task of response generation is usually focused on responses to short texts, such as tweets or a turn in a dialog. Here we present a novel task of producing a critical response to a long argumentative text, and s
Externí odkaz:
http://arxiv.org/abs/1909.00393
Autor:
Lavee, Tamar, Orbach, Matan, Kotlerman, Lili, Kantor, Yoav, Gretz, Shai, Dankin, Lena, Mirkin, Shachar, Jacovi, Michal, Bilu, Yonatan, Aharonov, Ranit, Slonim, Noam
Engaging in a live debate requires, among other things, the ability to effectively rebut arguments claimed by your opponent. In particular, this requires identifying these arguments. Here, we suggest doing so by automatically mining claims from a cor
Externí odkaz:
http://arxiv.org/abs/1907.11889