Zobrazeno 1 - 10
of 47
pro vyhledávání: '"Ganchev, Kuzman"'
Autor:
Malaviya, Chaitanya, Agrawal, Priyanka, Ganchev, Kuzman, Srinivasan, Pranesh, Huot, Fantine, Berant, Jonathan, Yatskar, Mark, Das, Dipanjan, Lapata, Mirella, Alberti, Chris
Experts in various fields routinely perform methodical writing tasks to plan, organize, and report their work. From a clinician writing a differential diagnosis for a patient, to a teacher writing a lesson plan for students, these tasks are pervasive
Externí odkaz:
http://arxiv.org/abs/2405.05938
Autor:
Huot, Fantine, Maynez, Joshua, Narayan, Shashi, Amplayo, Reinald Kim, Ganchev, Kuzman, Louis, Annie, Sandholm, Anders, Das, Dipanjan, Lapata, Mirella
While conditional generation models can now generate natural language well enough to create fluent text, it is still difficult to control the generation process, leading to irrelevant, repetitive, and hallucinated content. Recent work shows that plan
Externí odkaz:
http://arxiv.org/abs/2305.00034
Autor:
Bohnet, Bernd, Tran, Vinh Q., Verga, Pat, Aharoni, Roee, Andor, Daniel, Soares, Livio Baldini, Ciaramita, Massimiliano, Eisenstein, Jacob, Ganchev, Kuzman, Herzig, Jonathan, Hui, Kai, Kwiatkowski, Tom, Ma, Ji, Ni, Jianmo, Saralegui, Lierni Sestorain, Schuster, Tal, Cohen, William W., Collins, Michael, Das, Dipanjan, Metzler, Donald, Petrov, Slav, Webster, Kellie
Large language models (LLMs) have shown impressive results while requiring little or no direct supervision. Further, there is mounting evidence that LLMs may have potential in information-seeking scenarios. We believe the ability of an LLM to attribu
Externí odkaz:
http://arxiv.org/abs/2212.08037
The paper presents an approach to semantic grounding of language models (LMs) that conceptualizes the LM as a conditional model generating text given a desired semantic message formalized as a set of entity-relationship triples. It embeds the LM in a
Externí odkaz:
http://arxiv.org/abs/2211.09070
Autor:
Agrawal, Priyanka, Alberti, Chris, Huot, Fantine, Maynez, Joshua, Ma, Ji, Ruder, Sebastian, Ganchev, Kuzman, Das, Dipanjan, Lapata, Mirella
The availability of large, high-quality datasets has been one of the main drivers of recent progress in question answering (QA). Such annotated datasets however are difficult and costly to collect, and rarely exist in languages other than English, re
Externí odkaz:
http://arxiv.org/abs/2211.08264
Autor:
Narayan, Shashi, Maynez, Joshua, Amplayo, Reinald Kim, Ganchev, Kuzman, Louis, Annie, Huot, Fantine, Sandholm, Anders, Das, Dipanjan, Lapata, Mirella
The ability to convey relevant and faithful information is critical for many tasks in conditional generation and yet remains elusive for neural seq-to-seq models whose outputs often reveal hallucinations and fail to correctly cover important details.
Externí odkaz:
http://arxiv.org/abs/2207.00397
Publikováno v:
RANLP-2009
The paper presents a feature-rich approach to the automatic recognition and categorization of named entities (persons, organizations, locations, and miscellaneous) in news text for Bulgarian. We combine well-established features used for other langua
Externí odkaz:
http://arxiv.org/abs/2109.15121
A wide variety of neural-network architectures have been proposed for the task of Chinese word segmentation. Surprisingly, we find that a bidirectional LSTM model, when combined with standard deep learning techniques and best practices, can achieve b
Externí odkaz:
http://arxiv.org/abs/1808.06511
Autor:
Andor, Daniel, Alberti, Chris, Weiss, David, Severyn, Aliaksei, Presta, Alessandro, Ganchev, Kuzman, Petrov, Slav, Collins, Michael
We introduce a globally normalized transition-based neural network model that achieves state-of-the-art part-of-speech tagging, dependency parsing and sentence compression results. Our model is a simple feed-forward neural network that operates on a
Externí odkaz:
http://arxiv.org/abs/1603.06042
Entity type tagging is the task of assigning category labels to each mention of an entity in a document. While standard systems focus on a small set of types, recent work (Ling and Weld, 2012) suggests that using a large fine-grained label set can le
Externí odkaz:
http://arxiv.org/abs/1412.1820