106 citations · 239 across the 11 of their papers we have counts for
13 papers
DOLOMITES: Domain-Specific Long-Form Methodical Tasks
Chaitanya Malaviya, Priyanka Agrawal, Kuzman Ganchev +7
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…
Text-Blueprint: An Interactive Platform for Plan-based Conditional Generation
Fantine Huot, Joshua Maynez, Shashi Narayan +6
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 irrelev…
Attributed Question Answering: Evaluation and Modeling for Attributed Large Language Models
Bernd Bohnet, Vinh Q. Tran, Pat Verga +19
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 info…
Towards Computationally Verifiable Semantic Grounding for Language Models
Chris Alberti, Kuzman Ganchev, Michael Collins +2
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 for…
QAmeleon: Multilingual QA with Only 5 Examples
Priyanka Agrawal, Chris Alberti, Fantine Huot +6
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 c…
Conditional Generation with a Question-Answering Blueprint
Shashi Narayan, Joshua Maynez, Reinald Kim Amplayo +6
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 ofte…
Feature-Rich Named Entity Recognition for Bulgarian Using Conditional Random Fields
Georgi Georgiev, Preslav Nakov, Kuzman Ganchev +2
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…
State-of-the-art Chinese Word Segmentation with Bi-LSTMs
Ji Ma, Kuzman Ganchev, David Weiss
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 wi…
Globally Normalized Transition-Based Neural Networks
Daniel Andor, Chris Alberti, David Weiss +5
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.…
Context-Dependent Fine-Grained Entity Type Tagging
Dan Gillick, Nevena Lazic, Kuzman Ganchev +2
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…
Controlling Complexity in Part-of-Speech Induction
João V. Graça, Kuzman Ganchev, Luisa Coheur +2
We consider the problem of fully unsupervised learning of grammatical (part-of-speech) categories from unlabeled text. The standard maximum-likelihood hidden Markov model for this…
Multi-View Learning over Structured and Non-Identical Outputs
Kuzman Ganchev, Joao Graca, John Blitzer +1
In many machine learning problems, labeled training data is limited but unlabeled data is ample. Some of these problems have instances that can be factored into multiple views, eac…
Censored Exploration and the Dark Pool Problem
Kuzman Ganchev, Michael Kearns, Yuriy Nevmyvaka +1
We introduce and analyze a natural algorithm for multi-venue exploration from censored data, which is motivated by the Dark Pool Problem of modern quantitative finance. We prove th…