Zobrazeno 1 - 10
of 25
pro vyhledávání: '"Gashteovski, Kiril"'
Open-Domain Multi-Document Summarization (ODMDS) is crucial for addressing diverse information needs, which aims to generate a summary as answer to user's query, synthesizing relevant content from multiple documents in a large collection. Existing ap
Externí odkaz:
http://arxiv.org/abs/2406.12494
Autor:
Gioacchini, Luca, Siracusano, Giuseppe, Sanvito, Davide, Gashteovski, Kiril, Friede, David, Bifulco, Roberto, Lawrence, Carolin
The advances made by Large Language Models (LLMs) have led to the pursuit of LLM agents that can solve intricate, multi-step reasoning tasks. As with any research pursuit, benchmarking and evaluation are key corner stones to efficient and reliable pr
Externí odkaz:
http://arxiv.org/abs/2404.06411
Downstream applications often require text classification models to be accurate and robust. While the accuracy of the state-of-the-art Language Models (LMs) approximates human performance, they often exhibit a drop in performance on noisy data found
Externí odkaz:
http://arxiv.org/abs/2311.06647
Open Information Extraction (OIE) methods extract facts from natural language text in the form of ("subject"; "relation"; "object") triples. These facts are, however, merely surface forms, the ambiguity of which impedes their downstream usage; e.g.,
Externí odkaz:
http://arxiv.org/abs/2310.14909
Unlike traditional unsupervised clustering, semi-supervised clustering allows users to provide meaningful structure to the data, which helps the clustering algorithm to match the user's intent. Existing approaches to semi-supervised clustering requir
Externí odkaz:
http://arxiv.org/abs/2307.00524
Event detection is a crucial information extraction task in many domains, such as Wikipedia or news. The task typically relies on trigger detection (TD) -- identifying token spans in the text that evoke specific events. While the notion of triggers s
Externí odkaz:
http://arxiv.org/abs/2305.14163
Autor:
Widjaja, Haris, Gashteovski, Kiril, Rim, Wiem Ben, Liu, Pengfei, Malon, Christopher, Ruffinelli, Daniel, Lawrence, Carolin, Neubig, Graham
Knowledge Graphs (KGs) store information in the form of (head, predicate, tail)-triples. To augment KGs with new knowledge, researchers proposed models for KG Completion (KGC) tasks such as link prediction; i.e., answering (h; p; ?) or (?; p; t) quer
Externí odkaz:
http://arxiv.org/abs/2208.11024
Autor:
Kotnis, Bhushan, Gashteovski, Kiril, Gastinger, Julia, Serra, Giuseppe, Alesiani, Francesco, Sztyler, Timo, Shaker, Ammar, Gong, Na, Lawrence, Carolin, Xu, Zhao
With Human-Centric Research (HCR) we can steer research activities so that the research outcome is beneficial for human stakeholders, such as end users. But what exactly makes research human-centric? We address this question by providing a working de
Externí odkaz:
http://arxiv.org/abs/2207.04447
Autor:
Saralajew, Sascha, Shaker, Ammar, Xu, Zhao, Gashteovski, Kiril, Kotnis, Bhushan, Rim, Wiem Ben, Quittek, Jürgen, Lawrence, Carolin
With the rise of AI systems in real-world applications comes the need for reliable and trustworthy AI. An essential aspect of this are explainable AI systems. However, there is no agreed standard on how explainable AI systems should be assessed. Insp
Externí odkaz:
http://arxiv.org/abs/2205.12749
Autor:
Kotnis, Bhushan, Gashteovski, Kiril, Rubio, Daniel Oñoro, Rodriguez-Tembras, Vanesa, Shaker, Ammar, Takamoto, Makoto, Niepert, Mathias, Lawrence, Carolin
Open Information Extraction (OpenIE) is the task of extracting (subject, predicate, object) triples from natural language sentences. Current OpenIE systems extract all triple slots independently. In contrast, we explore the hypothesis that it may be
Externí odkaz:
http://arxiv.org/abs/2110.08144