Papers by Kuzman Ganchev
Dolomites: Domain-Specific Long-Form Methodical Tasks (2025.tacl-1)
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Chaitanya Malaviya, Priyanka Agrawal, Kuzman Ganchev, Pranesh Srinivasan, Fantine Huot, Jonathan Berant, Mark Yatskar, Dipanjan Das, Mirella Lapata, Chris Alberti
| Challenge: | Experts in various fields perform methodical writing tasks to plan, organize, and report their work. |
| Approach: | They propose a benchmark with specifications for 519 methodical writing tasks . they use expert revisions of up to 10 model-generated examples to evaluate contemporary language models. |
| Outcome: | The proposed benchmark includes specifications for 519 methodical writing tasks . it includes examples with input and output examples, and is available at https://dolomites-benchmark.github.io/ . |
State-of-the-art Chinese Word Segmentation with Bi-LSTMs (D18-1)
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| Challenge: | A wide variety of neural-network architectures have been proposed for the task of Chinese word segmentation. |
| Approach: | They propose a bidirectional LSTM model with standard deep learning techniques and best practices for the task of Chinese word segmentation. |
| Outcome: | The proposed model outperforms models based on standard deep learning techniques and best practices on Chinese word segmentation datasets. |
Conditional Generation with a Question-Answering Blueprint (2023.tacl-1)
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Shashi Narayan, Joshua Maynez, Reinald Kim Amplayo, Kuzman Ganchev, Annie Louis, Fantine Huot, Anders Sandholm, Dipanjan Das, Mirella Lapata
| Challenge: | Neural generation models often struggle to identify which content units are salient. |
| Approach: | They propose a new conceptualization of text plans as a sequence of question-answer pairs . they propose QA blueprints as QA proxy for content selection and planning . |
| Outcome: | The proposed model improves existing datasets with QA blueprints as proxy for content selection and planning. |
Text-Blueprint: An Interactive Platform for Plan-based Conditional Generation (2023.eacl-demo)
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Fantine Huot, Joshua Maynez, Shashi Narayan, Reinald Kim Amplayo, Kuzman Ganchev, Annie Priyadarshini Louis, Anders Sandholm, Dipanjan Das, Mirella Lapata
| Challenge: | Recent work shows that conditional generation models can be useful to control the text generation process, leading to irrelevant, repetitive, and hallucinated content. |
| Approach: | They propose a web browser-based demonstration for query-focused summarization that uses a sequence of question-answer pairs as a blueprint plan for guiding text generation. |
| Outcome: | The proposed model can be used to generate query-focused summarization text using question-answer pairs as a blueprint plan. |