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Found 12 papers, 5 papers with code
The NLP Task Effectiveness of Long-Range Transformers
Transformer models cannot easily scale to long sequences due to their O(N^2) time and space complexity.
Everything Is All It Takes: A Multipronged Strategy for Zero-Shot Cross-Lingual Information Extraction
Zero-shot cross-lingual information extraction (IE) describes the construction of an IE model for some target language, given existing annotations exclusively in some other language, typically English.
Learning How to Ask: Querying LMs with Mixtures of Soft Prompts
We explore the idea of learning prompts by gradient descent -- either fine-tuning prompts taken from previous work, or starting from random initialization.
LOME: Large Ontology Multilingual Extraction
We present LOME, a system for performing multilingual information extraction.
CopyNext: Explicit Span Copying and Alignment in Sequence to Sequence Models
Copy mechanisms are employed in sequence to sequence models (seq2seq) to generate reproductions of words from the input to the output.
Iterative Paraphrastic Augmentation with Discriminative Span Alignment
We introduce a novel paraphrastic augmentation strategy based on sentence-level lexically constrained paraphrasing and discriminative span alignment.
Neural Datalog Through Time: Informed Temporal Modeling via Logical Specification
Learning how to predict future events from patterns of past events is difficult when the set of possible event types is large.
Informed Temporal Modeling via Logical Specification of Factorial LSTMs
Consider a world in which events occur that involve various entities.
Imputing Missing Events in Continuous-Time Event Streams
On held-out incomplete sequences, our method is effective at inferring the ground-truth unobserved events, with particle smoothing consistently improving upon particle filtering.
Data2Text Studio: Automated Text Generation from Structured Data
Data2Text Studio is a platform for automated text generation from structured data.
Learning Latent Semantic Annotations for Grounding Natural Language to Structured Data
Previous work on grounded language learning did not fully capture the semantics underlying the correspondences between structured world state representations and texts, especially those between numerical values and lexical terms.
Inference of unobserved event streams with neural Hawkes particle smoothing
Particle smoothing is an extension of particle filtering in which proposed events are conditioned on the future as well as the past.
