Search Results for author:
Found 18 papers, 6 papers with code
DOLOMITES: Domain-Specific Long-Form Methodical Tasks
We develop a typology of methodical tasks structured in the form of a task objective, procedure, input, and output, and introduce DoLoMiTes, a novel benchmark with specifications for 519 such tasks elicited from hundreds of experts from across 25 fields.
Text-Blueprint: An Interactive Platform for Plan-based Conditional Generation
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.
Attributed Question Answering: Evaluation and Modeling for Attributed Large Language Models
We take human annotations as a gold standard and show that a correlated automatic metric is suitable for development.
Towards Computationally Verifiable Semantic Grounding for Language Models
Compared to a baseline that generates text using greedy search, we demonstrate two techniques that improve the fluency and semantic accuracy of the generated text: The first technique samples multiple candidate text sequences from which the semantic parser chooses.
QAmeleon: Multilingual QA with Only 5 Examples
The availability of large, high-quality datasets has been one of the main drivers of recent progress in question answering (QA).
Conditional Generation with a Question-Answering Blueprint
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.
Feature-Rich Named Entity Recognition for Bulgarian Using Conditional Random Fields
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.
State-of-the-art Chinese Word Segmentation with Bi-LSTMs
A wide variety of neural-network architectures have been proposed for the task of Chinese word segmentation.
Globally Normalized Transition-Based Neural Networks
Our model is a simple feed-forward neural network that operates on a task-specific transition system, yet achieves comparable or better accuracies than recurrent models.
Ranked #16 on
Dependency Parsing
on Penn Treebank
Efficient Inference and Structured Learning for Semantic Role Labeling
We present a dynamic programming algorithm for efficient constrained inference in semantic role labeling.
Context-Dependent Fine-Grained Entity Type Tagging
We propose the task of context-dependent fine type tagging, where the set of acceptable labels for a mention is restricted to only those deducible from the local context (e. g. sentence or document).
Controlling Complexity in Part-of-Speech Induction
We consider the problem of fully unsupervised learning of grammatical (part-of-speech) categories from unlabeled text.
Posterior vs Parameter Sparsity in Latent Variable Models
We apply this new method to learn first-order HMMs for unsupervised part-of-speech (POS) tagging, and show that HMMs learned this way consistently and significantly out-performs both EM-trained HMMs, and HMMs with a sparsity-inducing Dirichlet prior trained by variational EM.
