Search Results for author: Guanghui Qin

Found 12 papers, 5 papers with code

The NLP Task Effectiveness of Long-Range Transformers

no code implementations16 Feb 2022 Guanghui Qin, Yukun Feng, Benjamin Van Durme

Transformer models cannot easily scale to long sequences due to their O(N^2) time and space complexity.

Learning How to Ask: Querying LMs with Mixtures of Soft Prompts

2 code implementations NAACL 2021 Guanghui Qin, Jason Eisner

We explore the idea of learning prompts by gradient descent -- either fine-tuning prompts taken from previous work, or starting from random initialization.

Language Modelling

Iterative Paraphrastic Augmentation with Discriminative Span Alignment

no code implementations1 Jul 2020 Ryan Culkin, J. Edward Hu, Elias Stengel-Eskin, Guanghui Qin, Benjamin Van Durme

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

1 code implementation ICML 2020 Hongyuan Mei, Guanghui Qin, Minjie Xu, Jason Eisner

Learning how to predict future events from patterns of past events is difficult when the set of possible event types is large.

Imputing Missing Events in Continuous-Time Event Streams

2 code implementations14 May 2019 Hongyuan Mei, Guanghui Qin, Jason Eisner

On held-out incomplete sequences, our method is effective at inferring the ground-truth unobserved events, with particle smoothing consistently improving upon particle filtering.

Learning Latent Semantic Annotations for Grounding Natural Language to Structured Data

1 code implementation EMNLP 2018 Guanghui Qin, Jin-Ge Yao, Xuening Wang, Jinpeng Wang, Chin-Yew Lin

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.

Grounded language learning Text Generation

Inference of unobserved event streams with neural Hawkes particle smoothing

no code implementations27 Sep 2018 Hongyuan Mei, Guanghui Qin, Jason Eisner

Particle smoothing is an extension of particle filtering in which proposed events are conditioned on the future as well as the past.

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